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Data mining techniques for the detection of fraudulent financial statements

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A neural network approach for credit risk evaluation

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A data-driven approach to predict the success of bank telemarketing

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Review and comparison of methods to study the contribution of variables in artificial neural network models

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Desmystifying artificial intelligence What business leaders need to know about cognitive technologies

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Improving Credit Card Fraud Detection using a Meta-Learning Strategy

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Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

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An improved k-nearest neighbor algorithm and its application to high resolution remote sensing image classification

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Improving k nearest neighbor with exemplar generalization for imbalanced classification

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Soft Computing in the Basel II framework

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Las redes neuronales y la evaluación del riesgo de crédito

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Mapping the State of financial Stability

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Evolving intelligent systems: methodology and applications

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Exploring corporate bankruptcy with two-level self-organizing map

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Credit risk analysis applying logistic regression, neural networks and genetic algorithms models

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Comparative Evaluation of Predictive Modeling Techniques on Credit Card Data

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Using artificial neural networks analysis for small enterprise default prediction modeling: Statistical evidence from Italian firms

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An artificial neural network approach for credit risk management

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Dos aplicaciones empíricas de las Redes Neuronales Artificiales a la clasificación y la predicción financiera en el Mercado español

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Topic Sentiment Mining for Product Sales Performance Prediction

Author

Yuan, Hui and Xu, Wei and Lau, Raymond YK

Year

2015

Yahoo! for Amazon: Sentiment extraction from small talk on the web

Author

Das, Sanjiv R and Chen, Mike Y

Journal

Management Science

Year

2007

Volume

53

Number

9

Pages

1375–1388

Publisher

INFORMS

?` Es menester que los ingenieros filosofen?

Author

Aracil Santonja, Javier

Journal

Argumentos de razón técnica: Revista española de ciencia, tecnología y sociedad, y filosofía de la tecnología

Year

1999

Number

2

Pages

29–50

SOM and Feature Weights Based Method for Dimensionality Reduction in Large Gauss Linear Models

Author

Fernando Pavón, J. Vega, Sebastián Dormido Canto

Year

2015

Editor

Springer International Publishing Switzerland 2015

Volume

SLDS 2015, LNAI 9047, pp. 376–385, 2015.

Publisher

A. Gammerman et al. (Eds.)

Month

Abril

Rating

5

Read

1

Toward automatic time-series forecasting using neural networks.

Author

Yan, Weizhong

Journal

IEEE transactions on neural networks and learning systems

Year

2012

Volume

23

Number

7

Pages

1028–1039

Month

Jul

Abstract

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Doi

10.1109/TNNLS.2012.2198074

Issn

2162-2388

Language

eng

Url

http://www.ncbi.nlm.nih.gov/pubmed/24807130

Speech recognition with deep recurrent neural networks.

Author

Graves, Alex and rahman Mohamed, Abdel and Hinton, Geoffrey E.

Booktitle

{ICASSP}

Year

2013

Pages

6645–6649

Publisher

IEEE

Keywords

dblp

Added-At

2013-11-02T00:00:00.000+0100

Crossref

conf/icassp/2013

Interhash

1cd74ce6e8dae51149cd2b2d0f08f81a

Intrahash

8bb8304f2b2e3a21c4c40719b0c60dd5

Url

http://dblp.uni-trier.de/db/conf/icassp/icassp2013.html#GravesMH13; http://dx.doi.org/10.1109/ICASSP.2013.6638947; http://www.bibsonomy.org/bibtex/28bb8304f2b2e3a21c4c40719b0c60dd5/dblp

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Bibsonomy

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Author

Cheeseman, Peter

Booktitle

{IJCAI}

Year

1985

Editor

Joshi, Aravind K.

Pages

1002–1009

Publisher

Morgan Kaufmann

Keywords

dblp

Added-At

2012-05-15T00:00:00.000+0200

Crossref

conf/ijcai/1985

Interhash

603959f51070fb78311e969ded2dc36f

Intrahash

fee726a98bfba5816fd18e8d19e59109

Url

http://dblp.uni-trier.de/db/conf/ijcai/ijcai85.html#Cheeseman85; http://www.bibsonomy.org/bibtex/2fee726a98bfba5816fd18e8d19e59109/dblp

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Bibsonomy

A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics.

Author

Cauwenberghs, Gert

Booktitle

{NIPS}

Year

1993

Editor

Cowan, Jack D. and Tesauro, Gerald and Alspector, Joshua

Pages

858–865

Publisher

Morgan Kaufmann

Keywords

dblp

Abstract

dblp

Added-At

2003-05-28T00:00:00.000+0200

Crossref

conf/nips/1993

Date

2003-05-28

Interhash

04102cd1cfaff9805562906179a8bb49

Intrahash

25a7b882774e48e620633afe5c6970b7

Isbn

1-55860-322-0

Url

http://dblp.uni-trier.de/db/conf/nips/nips1993.html#Cauwenberghs93; http://nips.djvuzone.org/djvu/nips06/0858.djvu; http://www.bibsonomy.org/bibtex/225a7b882774e48e620633afe5c6970b7/dblp

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Bibsonomy

Scaling to Very Very Large Corpora for Natural Language Disambiguation.

Author

Banko, Michele and Brill, Eric

Booktitle

{ACL}

Year

2001

Pages

26–33

Publisher

Morgan Kaufmann Publishers

Keywords

dblp

Added-At

2011-12-23T00:00:00.000+0100

Crossref

conf/acl/2001

Interhash

6b6b98539e848e6d0fb9b427be12dd9e

Intrahash

f3e875335a6696d0efe8859304d312c4

Url

http://dblp.uni-trier.de/db/conf/acl/acl2001.html#BankoB01; http://www.aclweb.org/anthology/P01-1005; http://www.bibsonomy.org/bibtex/2f3e875335a6696d0efe8859304d312c4/dblp

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Bibsonomy

Algorithms for Better Representation and Faster Learning in Radial Basis Function Networks.

Author

Saha, Avijit and Keeler, James D.

Booktitle

{NIPS}

Year

1989

Editor

Touretzky, David S.

Pages

482–489

Publisher

Morgan Kaufmann

Keywords

dblp

Abstract

dblp

Added-At

2003-05-28T00:00:00.000+0200

Crossref

conf/nips/1989

Date

2003-05-28

Interhash

84cf6e1ecdf9af8e0dee5d499f14a2dc

Intrahash

0e3a90f0e6af8a528018e2d547120143

Isbn

1-55860-100-7

Url

http://dblp.uni-trier.de/db/conf/nips/nips1989.html#SahaK89; http://nips.djvuzone.org/djvu/nips02/0482.djvu; http://www.bibsonomy.org/bibtex/20e3a90f0e6af8a528018e2d547120143/dblp

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Bibsonomy

Self-Organising Artificial Neural Networks.

Author

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Booktitle

{IWANN}

Year

1995

Editor

Mira, José and Hernández, Francisco Sandoval

Volume

930

Pages

322–329

Series

{Lecture Notes in Computer Science}

Publisher

Springer

Keywords

dblp

Abstract

dblp

Added-At

2009-09-26T00:00:00.000+0200

Crossref

conf/iwann/1995

Date

2009-09-26

Interhash

14b2f3689f316e93ee138016dbee32a0

Intrahash

66b73bb128458d089325d5ccddf48097

Isbn

3-540-59497-3

Url

http://dblp.uni-trier.de/db/conf/iwann/iwann1995.html#FlanaganH95; http://dx.doi.org/10.1007/3-540-59497-3_192; http://www.bibsonomy.org/bibtex/266b73bb128458d089325d5ccddf48097/dblp

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Bibsonomy

Detection of cluster in Self-Organizing Maps for controlling a prostheses using nerve signals.

Author

Bogdan, Martin and Rosenstiel, Wolfgang

Booktitle

{ESANN}

Year

2001

Pages

131–136

Keywords

dblp

Abstract

dblp

Added-At

2006-07-26T00:00:00.000+0200

Crossref

conf/esann/2001

Date

2006-07-26

Interhash

64f8b7ef08f7fc8b95a9568964a1119e

Intrahash

859b7fe586ef41f9eccacfab58f05441

Url

http://dblp.uni-trier.de/db/conf/esann/esann2001.html#BogdanR01; http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2001-17.pdf; http://www.bibsonomy.org/bibtex/2859b7fe586ef41f9eccacfab58f05441/dblp

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Bibsonomy

Big data: The next frontier for innovation, competition, and productivity

Author

Manyika, James and Chui, Michael and Brown, Brad and Bughin, Jacques and Dobbs, Richard and Roxburgh, Charles and Byers, Angela Hung

Year

2011

Month

Junio

Editor

Institute, McKinsey Global

Publisher

McKinsey Global Institute

Optimal Radial Basis Function nets with Applications to Nonlinear Function Learning and Classification

Author

Krzyzak, Adam and Xu, Ley

Year

1996

Editor

ICONIP‘96

Location

Hong Kong

Big Data. El reto de tratar de forma efectiva una ingente cantidad de información

Author

García, Emilia

Journal

Boletic 65

Year

2013

Month

Abril

The Big Data Opportunity

Author

Yiu, Chris

Institution

Police Exchange

Year

2012

Editor

Exchange, Police

Isbn

978-1-907689-22-2

IDC12

The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far Est

Author

Gantz, John and Reintzel, David

Institution

IDC

Year

2012

Month

Diciembre

Quantum Artificial Intelligence : A Survey of Application of Quantum Physics in Artificial Intelligence.

Author

Manchanda, Priyanka

Journal

CoRR

Year

2013

Volume

abs/1309.7173

Month

Septiembre

Keywords

dblp

Added-At

2013-10-16T00:00:00.000+0200

Interhash

a70ab71de679a88dcc8c484c930a68d0

Intrahash

113a0f66bb43560561dafa57830a8f24

Url

http://dblp.uni-trier.de/db/journals/corr/corr1309.html#Manchanda13; http://arxiv.org/abs/1309.7173; http://www.bibsonomy.org/bibtex/2113a0f66bb43560561dafa57830a8f24/dblp

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Bibsonomy

Learning in the Model Space for Fault Diagnosis

Author

Chen, Huanhuan and Tino, Peter and Yao, Xin and Rodan, Ali

Journal

CoRR

Year

2012

Volume

abs/1210.8291

Month

Noviembre

Keywords

dblp

Added-At

2012-11-02T00:00:00.000+0100

Interhash

a81e81d4b92e74b620e398d0e65efad4

Intrahash

ce5b109c496a123610df35bea00bba7b

Url

http://dblp.uni-trier.de/db/journals/corr/corr1210.html#abs-1210-8291; http://arxiv.org/abs/1210.8291; http://www.bibsonomy.org/bibtex/2ce5b109c496a123610df35bea00bba7b/dblp

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Bibsonomy

Big Data. Now’s the time to create business value with data

Year

2013

Month

Junio

Journal

Innovation edge

Publisher

BBVA

The discipline of machine learning

Author

Mitchell, Tom

Year

2006

Number

CMU ML-06 108

Keywords

machine-learning mitchell

Added-At

2009-03-25T11:53:39.000+0100

Interhash

fb1a95a1334ab8be85ffb2749ca10d8b

Intrahash

55bb706fdd667a0d0a76b78b46f6fabe

Url

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Bibsonomy

Statistics and Data Mining: Intersecting Disciplines.

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SIGKDD Explorations

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1

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1

Pages

16–19

Keywords

dblp

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2011-12-06T00:00:00.000+0100

Cdrom

sigkdd1-1/P16.pdf

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e7333d3f3d8cfc15b91402c3c54836e7

Intrahash

5467d1b1a12243f527c5b58e5ead8806

Url

http://dblp.uni-trier.de/db/journals/sigkdd/sigkdd1.html#Hand99; http://doi.acm.org/10.1145/846170.846171; http://www.bibsonomy.org/bibtex/25467d1b1a12243f527c5b58e5ead8806/dblp

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Bibsonomy

Computing Machinery and Intelligence

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Journal

Mind

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1950

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59

Pages

433–460

Keywords

2000 book nlp

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2008-03-17T11:27:54.000+0100

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3f7a151a4f79fe75b4bb148b41279a9b

Intrahash

84f8dcb41f0c8f26e10641a347e30ab0

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http://www.bibsonomy.org/bibtex/284f8dcb41f0c8f26e10641a347e30ab0/nlp

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Bibsonomy

From Data Mining to Knowledge Discovery in Databases

Author

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Journal

AI Magazine

Year

1996

Pages

37–54

Keywords

imported

Abstract

The big one

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2006-03-09T15:13:30.000+0100

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971272cb912769da4f85aab25536354b

Intrahash

eb5f2e6742d9520453cbf9d100cacfd2

Url

http://www.bibsonomy.org/bibtex/2eb5f2e6742d9520453cbf9d100cacfd2/diana

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Bibsonomy

Artificial Intelligence: A Modern Approach

Publisher

Prentice Hall

Year

2010

Author

Russell, Stuart and Norvig, Peter

Edition

3

Keywords

ai

Added-At

2010-05-11T15:41:52.000+0200

Interhash

53908a52dd4c6c8e39f93f4ffc8341be

Intrahash

0533b732950d1c5ab4ac12d4f32fe637

Url

http://www.bibsonomy.org/bibtex/20533b732950d1c5ab4ac12d4f32fe637/emanuel

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Bibsonomy

Artificial Intelligence The Very Idea

Publisher

MIT Press

Year

1985

Author

Haugeland, John

Keywords

AT15

Added-At

2010-03-31T13:53:05.000+0200

Interhash

e6b5935bbb6f0af3c45b87bb9cb99ec1

Intrahash

55cad6c2b7d7e32bdb6be27930f2850e

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Bibsonomy

An introduction to artificial intelligence. Can computers think?

Publisher

Boyd \& Fraser Pub. Co.

Year

1978

Author

Bellman, Richard

Introduction to Artificial Intelligence

Publisher

Addison Wesley

Year

1985

Author

Charniak, Eugene and McDermott, Drew

Keywords

2000 book nlp

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2008-03-17T11:27:54.000+0100

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66b334b5be8c784f63c6347024b6897e

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5f6b3e3f56905d5f49d56c89fd01b159

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Bibsonomy

Artificial intelligence (3. ed.).

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Year

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Author

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dblp

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2011-04-07T00:00:00.000+0200

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40c5a102f4effdc20ae295d3371a260d

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501433958715a41de58ec1f6d322ab5a

Isbn

978-0-201-53377-4

Pages

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Url

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Bibsonomy

Age of intelligent machines.

Publisher

MIT Press

Year

1992

Author

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Keywords

dblp

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2011-05-02T00:00:00.000+0200

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7b06abb5cf3b307870c9ec094f8a723a

Intrahash

0f8d983e8d638911f7e677bcfc336825

Isbn

978-0-262-61079-7

Pages

I–XIII, 1–565

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Year

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dblp

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2011-04-19T00:00:00.000+0200

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e9f76138c3ea70a3401404c3f56ed528

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e7b72a59dbb05880d2939e536a68bd9b

Isbn

978-0-07-052263-3

Pages

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Bibsonomy

Computational intelligence - a logical approach.

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Oxford University Press

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1998

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dblp

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2011-04-18T00:00:00.000+0200

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af5e9dd2276088fc416ce46b3470e93e

Intrahash

8eabe3f44ef46eec87ed00535f2241f2

Isbn

978-0-19-510270-3

Pages

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Bibsonomy

Artificial Intelligence: A New Synthesis.

Publisher

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Year

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Address

San Mateo, CA

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imported

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2009-06-01T13:37:43.000+0200

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3a09423e77c85c0ca9d7c135ef1a60ce

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87f1d08d47ab68f8e405e040069dc8d5

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A logical calculus of the ideas immanent in nervous activity

Author

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Year

1943

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Keywords

nnets

Abstract

CCNLab BibTeX

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2008-09-16T23:39:07.000+0200

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f9ac013a9ed96a36c8771b3a1ce71702

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d6c807e181a4e6b9a6ae974c9922b477

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McCulloch and Pitts

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Bibsonomy

The Organization of Behavior

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John Wiley

Year

1949

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New York

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imported

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diverse cognitive systems bib

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2009-06-26T15:25:19.000+0200

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a250a4a84c657534369c150fa5eb69cd

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4d1cea5a8dab04cb2084a38b98ebdbde

Owner

martin

Timestamp

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http://www.bibsonomy.org/bibtex/24d1cea5a8dab04cb2084a38b98ebdbde/butz

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Bibsonomy

Perceptrons

Publisher

MIT Press

Year

1969

Author

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Address

Cambridge, MA

Keywords

critic minsky papert perceptron

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2013-12-05T10:32:26.000+0100

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9ad5b73f68093070d73e54312145eca2

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4587aec0472c41d00c38bf3e888304ba

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A proposal for the Dartmouth summer research project on Artificial Intelligence

Howpublished

http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

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McCarthy, J. and Minsky, M. L. and Rochester, N. and Shannon, C.E.

Year

1955

Keywords

ai

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2011-10-17T13:15:37.000+0200

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8f661d303ba4257e6654deb8ce85d2ea

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bb04851e0b713bbc64e609c7d3b90d34

Url

http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html; http://www.bibsonomy.org/bibtex/2bb04851e0b713bbc64e609c7d3b90d34/mhwombat

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Bibsonomy

DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation.

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Journal

Artif. Intell.

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1993

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2

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209–261

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dblp

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2011-06-30T00:00:00.000+0200

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ccaa2c9a7283629a85530070ee8e67ca

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85a8abd113718d7cc30062a2ac31befc

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http://dblp.uni-trier.de/db/journals/ai/ai61.html#LindsayBFL93; http://dx.doi.org/10.1016/0004-3702(93)90068-M; http://www.bibsonomy.org/bibtex/285a8abd113718d7cc30062a2ac31befc/dblp

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R1: A rule-based configurator of computer systems

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McDermott, John

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Artificial Intelligence

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1982

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19

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1

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39–88

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configurator

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2011-07-12T21:57:30.000+0200

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df5aeff680e9460fb1c8b0adf7fc3476

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acf79364f94b78f0e2298bbb4e1b340d

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Bibsonomy

Applied Optimal Control

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Blaisdell

Year

1969

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New York

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nnets

Abstract

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2008-09-16T23:39:07.000+0200

Biburl

http://www.bibsonomy.org/bibtex/27d13cce517ce39632aeabf713bc6fbeb/brian.mingus

Booktitle

{Applied Optimal Control}

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3f3142a9345a8acc8c96f47a01da5372

Intrahash

7d13cce517ce39632aeabf713bc6fbeb

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Parallel Distributed Processing

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MIT Press

Year

1986

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Volume

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Bibsonomy

Statistical Inference For Probabilistic Functions Of Finite State Markov Chains

Author

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Journal

Ann. Math. Statist.

Year

1966

Volume

37

Number

6

Pages

1554–1563

Keywords

CTII:WS1213 chain markov master uni ws1213

Abstract

Baum , Petrie : Statistical Inference for Probabilistic Functions of Finite State Markov Chains

Added-At

2012-11-13T01:17:35.000+0100

Interhash

851eea91e34267b918104100f5871896

Intrahash

3e2a7af785f809fd5eb1b0df7d8afeac

Url

http://www.bibsonomy.org/bibtex/23e2a7af785f809fd5eb1b0df7d8afeac/telekoma

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An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology

Author

Baum, Leonard E. and Eagon, J. A.

Journal

Bulletin of the American Mathematical Society

Year

1967

Volume

73

Number

3

Pages

360–363

Month

Mayo

Keywords

2000 book nlp

Added-At

2008-03-17T11:27:54.000+0100

Interhash

633f8c9a36f9f9d997be1884cfdcae68

Intrahash

6835e3cada52062981333f00751604d4

Url

http://www.bibsonomy.org/bibtex/26835e3cada52062981333f00751604d4/nlp

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Bibsonomy

Probabilistic Reasoning in Intelligent Systems

Publisher

Morgan Kaufmann

Year

1988

Author

Pearl, J.

Keywords

imported

Added-At

2008-10-07T16:03:39.000+0200

Interhash

406f8f788630ae4a8008631cc51043ca

Intrahash

9bdd1ef80df69aef150b13248b62907c

Url

http://www.bibsonomy.org/bibtex/29bdd1ef80df69aef150b13248b62907c/brefeld

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Bibsonomy

Soar: An architecture for general intelligence

Author

Laird, J. E. and Newell, A. and Rosenbloom, P. S.

Journal

Artificial Intelligence

Year

1987

Volume

33(1)

Pages

1–64

Keywords

imported

Added-At

2006-03-28T18:27:22.000+0200

Interhash

fb8bdb44b940b745b596202e41720ed4

Intrahash

4b002227ef997ff77b146b76db347864

Url

http://www.bibsonomy.org/bibtex/24b002227ef997ff77b146b76db347864/maksim

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Bibsonomy

The St. Thomas Common Sense Symposium: Designing Architectures for Human-Level Intelligence.

Author

Minsky, Marvin and Singh, Push and Sloman, Aaron

Journal

AI Magazine

Year

2004

Volume

25

Number

2

Pages

113–124

Keywords

dblp

Added-At

2010-12-29T00:00:00.000+0100

Interhash

b4056ec42ebdc5efd6fe67413d21d90e

Intrahash

47a6442bb66fff7fe8e38647d175e76c

Url

http://dblp.uni-trier.de/db/journals/aim/aim25.html#MinskySS04; http://www.aaai.org/ojs/index.php/aimagazine/article/view/1764; http://www.bibsonomy.org/bibtex/247a6442bb66fff7fe8e38647d175e76c/dblp

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Artificial General Intelligence

Publisher

Springer

Year

2007

Editor

Goertzel, Ben and Pennachin, Cassio

Series

{Cognitive Technologies}

Keywords

dblp

Added-At

2013-09-10T00:00:00.000+0200

Booktitle

{Artificial General Intelligence}

Interhash

bc9c03644ea1833c1068f10eacd44ee7

Intrahash

84408c397ceec666f6f52ca72b09669b

Isbn

978-3-540-23733-4

Url

http://dblp.uni-trier.de/db/series/cogtech/354023733.html; http://dx.doi.org/10.1007/978-3-540-68677-4; http://www.bibsonomy.org/bibtex/284408c397ceec666f6f52ca72b09669b/dblp

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Bibsonomy

Artificial Intelligence as a Positive and Negative Factor in Global Risk.

Author

Yudkowsky, E.

Journal

Oxford University Press

Year

2008

The Basic AI Drives

Author

Omohundro, S.

Year

2008

Journal

Appeared in AGI-08 - Proceedings of the First Conference on Artificial General Intelligence

OLeary13

Artificial Intelligence and Big Data

Author

O’Leary, D.E.

Journal

Intelligent Systems, IEEE

Year

2013

Volume

28

Number

2

Pages

96–99

Keywords

artificial intelligence; data analysis; AI Innovation in Industry; IEEE Intelligent Systems; artificial intelligence; big data analysis; big data capturing; big data structuring; Artificial intelligence; Data handling; Data storage systems; Information management; Internet; Machine learning algorithms; AI; artificial intelligence; big data; intelligent systems; parallelization; visualization

Abstract

AI Innovation in Industry is a new department for IEEE Intelligent Systems, and this paper examines some of the basic concerns and uses of AI for big data (AI has been used in several different ways to facilitate capturing and structuring big data, and it has been used to analyze big data for key insights).

Arnumber

6547979

Doi

10.1109/MIS.2013.39

Issn

1541-1672

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Managing Big Data for Scientific Visualization

Author

Cox, M. and Ellsworth, D.

Year

1997

Pages

5–1–5–17

Publisher

ACM Siggraph

Harness the Power of Big Data – The IBM Big Data Platform

Publisher

Mcgraw-Hill

Year

2012

Author

Zikopoulos, P. and deRoos, D. and Parasuraman, K. and Deutsch, T. and Giles, J. and Corrigan, D.

Keywords

bigdata ibm

Added-At

2013-06-22T14:17:15.000+0200

Interhash

11486deb14679ffaae3ad2dacc68e273

Intrahash

e0d012c31ad8d1624fba3159a62636db

Isbn

9780071808187

Url

http://www.bibsonomy.org/bibtex/2e0d012c31ad8d1624fba3159a62636db/sb3000

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MapReduce: simplified data processing on large clusters

Author

Dean, Jeffrey and Ghemawat, Sanjay

Booktitle

{In OSDI{\rq}04: Proceedings of the 6th conference on Symposium on Opearting Systems Design \& Implementation}

Year

2004

Publisher

USENIX Association

Keywords

mapreduce parallel\_programming

Added-At

2012-05-12T17:40:19.000+0200

Biburl

http://www.bibsonomy.org/bibtex/26abc55ecdd52d5b841e908ad62a33195/lopusz_kdd

Interhash

c853fc61c156362ffecdf9302fe7c33f

Intrahash

6abc55ecdd52d5b841e908ad62a33195

X-Fetchedfrom

Bibsonomy

Design of parallel hardware neural network systems from custom analog VLSI ‘building block’ chips

Author

Eberhardt, S. and Duong, T. and Thakoor, A.

Booktitle

{Neural Networks, 1989. IJCNN., International Joint Conference on}

Year

1989

Pages

183–190 vol.2

Keywords

CMOS integrated circuits; VLSI; analogue computer circuits; application specific integrated circuits; hybrid computers; learning systems; neural nets; parallel architectures; CMOS VLSI; arbitrary architectures; building-block components; custom analog VLSI; feedforward hardware; learning algorithms; multiplexer input neuron chip; multiplier circuits; neural network systems; neurons; on-chip capacitors; parallel hardware; sigmoidal activation function; stored analog charges; synapse chip design; variable-gain neuron chip; Analog circuits; Application specific integrated circuits; CMOS integrated circuits; Learning systems; Neural networks; Parallel architectures; Very-large-scale integration

Abstract

Hardware to implement feedforward neural networks has been developed for the evaluation of learning algorithms and prototyping of applications. To allow the construction of networks with arbitrary architectures, CMOS VLSI building-block components (e.g. arrays of neurons and synapses) have been designed. These can be cascaded to form networks with hundreds of neurons per layer. A 64-channel multiplexer input neuron chip serves to buffer stored charges for injection into the first synaptic layer. A 32*32 synapse chip design uses multiplier circuits to generate a conductance from stored analog charges representing weights. A 32-channel variable-gain neuron chip applies an adjustable-gain sigmoidal activation function to the sum of currents from the previous synaptic layer. Learning is performed by a host computer that can download weights and inputs onto the feedforward hardware, and read resultant network outputs. Weights and input values are stored as charges on on-chip capacitors; these are serially and invisibly refreshed by off-chip circuits that convert values stored in digital memory into analog signals.<<ETX>>

Arnumber

118697

Doi

10.1109/IJCNN.1989.118697

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Application of the ANNA neural network chip to high-speed character recognition.

Author

Säckinger, Eduard and Boser, Bernhard E. and Bromley, Jane and LeCun, Yann and Jackel, Lawrence D.

Journal

IEEE Transactions on Neural Networks

Year

1992

Volume

3

Number

3

Pages

498–505

Keywords

dblp

Added-At

2013-11-05T00:00:00.000+0100

Interhash

626ad3df52edeb6832e71366a01c3897

Intrahash

287b6cef5810509885719cd853073c9f

Url

http://dblp.uni-trier.de/db/journals/tnn/tnn3.html#SackingerBBLJ92; http://dx.doi.org/10.1109/72.129422; http://www.bibsonomy.org/bibtex/2287b6cef5810509885719cd853073c9f/dblp

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Bibsonomy

Big Data Artificial Intelligence

Author

Dervojeda, Kristina and Verzijl, Diederik and Nagtegaal, Fabian and Lengton, Mark and Rouwmaat, Elco

Month

Septiembre

Year

2013

Institution

UE

Organization

Business Innovation Observatory

Worldwide Big Data Technology and Services 2013–2017 Forecast

Author

Vesset, Dan and Nadkarni, Ashish and Brothers, Rob and Christiansen, Christian A. and Conway, Steve and others

Year

2013

Month

Diciembre

Organization

IDC

Unsupervised Word Sense Disambiguation Rivaling Supervised Methods

Author

Yarowsky, David

Booktitle

{In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics}

Year

1995

Pages

189–196

Keywords

citie

Added-At

2010-03-02T09:12:22.000+0100

Interhash

c73cf2b7ab3a6a8e2658e75559123761

Intrahash

0172267a15259a80af504c81ae6ef217

Url

http://www.bibsonomy.org/bibtex/20172267a15259a80af504c81ae6ef217/pkluegl

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Bibsonomy

Scene Completion Using Millions of Photographs

Author

Hays, James and Efros, Alexei A

Journal

ACM Transactions on Graphics (SIGGRAPH 2007)

Year

2007

Volume

26

Number

3

Keywords

completion graphics photograph scene

Abstract

What can you do with a million images? In this paper we present a new image completion algorithm powered by a huge database of photographs gathered from the Web. The algorithm patches up holes in images by finding similar image regions in the database that are not only seamless but also semantically valid. Our chief insight is that while the space of images is effectively infinite, the space of semantically differentiable scenes is actually not that large. For many image completion tasks we are able to find similar scenes which contain image fragments that will convincingly complete the image. Our algorithm is entirely data-driven, requiring no annotations or labelling by the user. Unlike existing image completion methods, our algorithm can generate a diverse set of image completions and we allow users to select among them. We demonstrate the superiority of our algorithm over existing image completion approaches.

Added-At

2007-08-21T08:14:17.000+0200

Interhash

14b4c5d079014159245fbeb4691cd3e4

Intrahash

2ecc9437051abcfb07bb54201894c08c

Url

http://graphics.cs.cmu.edu/projects/scene-completion; http://www.bibsonomy.org/bibtex/22ecc9437051abcfb07bb54201894c08c/jaeschke

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Essentials of the self-organizing map.

Author

Kohonen, Teuvo

Journal

Neural Networks

Year

2013

Volume

37

Pages

52–65

Keywords

dblp

Added-At

2013-04-13T00:00:00.000+0200

Interhash

763c50986a63b45e481e7873407e4456

Intrahash

148f11e9b2562c67f1bae5677bfec935

Url

http://dblp.uni-trier.de/db/journals/nn/nn37.html#Kohonen13; http://dx.doi.org/10.1016/j.neunet.2012.09.018; http://www.bibsonomy.org/bibtex/2148f11e9b2562c67f1bae5677bfec935/dblp

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Demystifying Big Data, A practical guide to transforming the business of government

Institution

TechAmerica Foundation

Year

2013

Address

601 Pennsylvania Avenue, N.W. North Building, Suite 600

Location

Washington, D.C. 20004

Organization

TechAmerica Foundation’s Federal Big Data Commision

N2Sky - Neural networks as services in the clouds

Author

Schikuta, E. and Mann, E.

Booktitle

{Neural Networks (IJCNN), The 2013 International Joint Conference on}

Year

2013

Pages

1–8

Month

Aug

Keywords

cloud computing; neural nets; organisational aspects; service-oriented architecture; simulation; N2Sky system; RAVO reference architecture; cloud-based neural network simulation; service oriented architectures; sky computing; virtual organization environment; Cloud computing; Communities; Computational modeling; Computer architecture; Neural networks; Software as a service; Training

Abstract

We present the N2Sky system, which provides a framework for the exchange of neural network specific knowledge, as neural network paradigms and objects, by a virtual organization environment. It follows the sky computing paradigm delivering ample resources by the usage of federated Clouds. N2Sky is a novel Cloud-based neural network simulation environment, which follows a pure service oriented approach. The system implements a transparent environment aiming to enable both novice and experienced users to do neural network research easily and comfortably. N2Sky is built using the RAVO reference architecture of virtual organizations which allows itself naturally integrating into the Cloud service stack (SaaS, PaaS, and IaaS) of service oriented architectures.

Arnumber

6707113

Doi

10.1109/IJCNN.2013.6707113

Issn

2161-4393

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Universal Approximation Using Radial-Basis-Function Networks

Author

Park, J. and Sandberg, I. W.

Journal

Neural Computation

Year

1991

Volume

3

Pages

246–257

Keywords

nn

Added-At

2008-03-11T14:52:34.000+0100

Citeulike-Article-Id

2378496

Interhash

1d44fc38e3ab73b4d29f822583e8697c

Intrahash

9be78289a54ec9731a41cbd7e2a47745

Priority

2

Url

http://www.bibsonomy.org/bibtex/29be78289a54ec9731a41cbd7e2a47745/idsia

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Bibsonomy

A new look at the statistical model identification

Author

Akaike, H.

Journal

IEEE Transactions on Automatic Control

Year

1974

Volume

19

Pages

716–723

Keywords

imported

Added-At

2009-01-22T02:55:58.000+0100

Biburl

http://www.bibsonomy.org/bibtex/2ca3d04061acea17ac1c29c000f8a4e82/stephane.guindon

Interhash

1e2509091dcc7474052e2ffdb5bbb51f

Intrahash

ca3d04061acea17ac1c29c000f8a4e82

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Bibsonomy

Learning by Combining Memorization and Gradient Descent

Author

Platt, John C.

Journal

Advances in Neural Information Processing Systems

Year

1991

Volume

3

Pages

715–720

Editor

Touretzky, D. S.

Publisher

Morgan Kaufmann, San Mateo

Approximation and Radial-Basis-Function Networks

Author

Park, J. and Sandeberg, I. W.

Journal

Neural Computation

Year

1993

Volume

5

Pages

305–316

A Theory of Networks for Approximation and Learning

Author

Poggio, T. and Girosi, F.

Journal

Proceedings of IEEE

Year

1990

Volume

78

Pages

1481–1497

Multi-layer feedforward networks are universal approximators

Author

Hornik, K. and Stinchcombe, M. and White, H.

Journal

Neural Networks

Year

1989

Volume

2

Pages

359–366

Universal approximation bounds for Superpositions of a Sigmoidal

Author

Barron, A. R.

Journal

IEEE Transactions on Information Theory

Year

1993

Volume

39

Pages

930–944

Sequential adaptation of radial basis function neural networks and its application to time-series prediction

Author

Kadirkamanathan, V. and Niranjan, M. and Fallside, F.

Journal

Advances in Neural Information Processing Systems

Year

1991

Volume

3

Pages

721–727

Editor

Touretzky, D. S.

Publisher

Morgan Kaufman, San Mateo

A Resource-Allocating Network for Function Interpolation

Author

Platt, John

Journal

Neural Computation

Year

1991

Volume

3

Number

2

Pages

213–225

Density-Based Multiscale Data Condensation.

Author

Mitra, Pabitra and Murthy, C. A. and Pal, Sankar K.

Journal

IEEE Trans. Pattern Anal. Mach. Intell.

Year

2002

Volume

24

Number

6

Pages

734–747

Keywords

dblp

Added-At

2011-11-07T00:00:00.000+0100

Interhash

df635e2aaf3a54c632de4a49dcf12d1a

Intrahash

58dd47da6d9346147954adb380c296c7

Url

http://dblp.uni-trier.de/db/journals/pami/pami24.html#MitraMP02a; http://doi.ieeecomputersociety.org/10.1109/TPAMI.2002.1008381; http://www.bibsonomy.org/bibtex/258dd47da6d9346147954adb380c296c7/dblp

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Variable selection using neural-network models.

Author

Castellano, Giovanna and Fanelli, Anna Maria

Journal

Neurocomputing

Year

2000

Volume

31

Number

1-4

Pages

1–13

Keywords

dblp

Abstract

dblp

Added-At

2003-05-12T00:00:00.000+0200

Date

2003-05-12

Interhash

63e14945e8c7a65bf8d496dacaaccd4d

Intrahash

8671100c06cf63afd3e61026200b1e0a

Url

http://dblp.uni-trier.de/db/journals/ijon/ijon31.html#CastellanoF00; http://dx.doi.org/10.1016/S0925-2312(99)00146-0; http://www.bibsonomy.org/bibtex/28671100c06cf63afd3e61026200b1e0a/dblp

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Bibsonomy

Neural Networks: A Comprehensive Foundation

Publisher

Prentice Hall

Year

1999

Author

Haykin, S.

Added-At

2012-08-18T21:00:48.000+0200

Biburl

http://www.bibsonomy.org/bibtex/22cb936e805c7c06a12a35525bc1ccf7b/dalbem

Groups

public

Interhash

9c833e39d6ac9c0a31aca034fb641190

Intrahash

2cb936e805c7c06a12a35525bc1ccf7b

Username

dalbem

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Bibsonomy

The Improved SOM-Based Dimensionality Reducton Method for KNN Classifier Using Weighted Euclidean Metric

Author

Wu, Jiunn-Lin and Li, I-Jing

Journal

International Journal of Computer, Consumer and Control (IJ3C)

Year

2014

Volume

3

Number

1

Neural-network design for small training sets of high dimension

Author

Yuan, Jen-Lun and Fine, T.L.

Journal

Neural Networks, IEEE Transactions on

Year

1998

Volume

9

Number

2

Pages

266–280

Month

Mar

Keywords

learning (artificial intelligence); load forecasting; neural net architecture; nonparametric statistics; statistical analysis; difference-based variance estimation; electric power demand; generalization performance; high dimension small training sets; network architecture; neural network design; nonparametric statistical process; projection pursuit regression; short-term forecasting; slicing inverse regression; statistically based methodology; Bayesian methods; Design methodology; Economic forecasting; Input variables; Load forecasting; Neural networks; Power generation economics; Power industry; Statistics; Training data

Abstract

We introduce a statistically based methodology for the design of neural networks when the dimension d of the network input is comparable to the size n of the training set. If one proceeds straightforwardly, then one is committed to a network of complexity exceeding n. The result will be good performance on the training set but poor generalization performance when the network is presented with new data. To avoid this we need to select carefully the network architecture, including control over the input variables. Our approach to selecting a network architecture first selects a subset of input variables (features) using the nonparametric statistical process of difference-based variance estimation and then selects a simple network architecture using projection pursuit regression (PPR) ideas combined with the statistical idea of slicing inverse regression (SIR). The resulting network, which is then retrained without regard to the PPR/SIR determined parameters, is one of moderate complexity (number of parameters significantly less than n) whose performance on the training set can be expected to generalize well. The application of this methodology is illustrated in detail in the context of short-term forecasting of the demand for electric power from an electric utility

Arnumber

661122

Doi

10.1109/72.661122

Issn

1045-9227

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A class of neural networks for independent component analysis.

Author

Karhunen, Juha and Oja, Erkki and Wang, Liuyue and Vigário, Ricardo and Joutsensalo, Jyrki

Journal

IEEE Transactions on Neural Networks

Year

1997

Volume

8

Number

3

Pages

486–504

Keywords

dblp

Added-At

2013-11-05T00:00:00.000+0100

Interhash

e352e95d46c24ac567fa843271c8761c

Intrahash

acd6f053f67f00557b7c3fe9fddc86e2

Url

http://dblp.uni-trier.de/db/journals/tnn/tnn8.html#KarhunenOWVJ97; http://dx.doi.org/10.1109/72.572090; http://www.bibsonomy.org/bibtex/2acd6f053f67f00557b7c3fe9fddc86e2/dblp

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Bibsonomy

Principal Components, Minor Components, and Linear Neural Networks

Author

Oja, Erkki

Journal

Neural Networks

Year

1992

Volume

5

Pages

927–935

A simple, homogeneous parallel PCA network

Author

Fyfe, C.

Booktitle

{Neural Networks, 1993. IJCNN ‘93-Nagoya. Proceedings of 1993 International Joint Conference on}

Year

1993

Volume

3

Pages

2496–2499 vol.3

Month

Oct

Keywords

neural nets; parallel algorithms; interneurons; neural nets; principal component analysis; simple homogeneous parallel PCA network; Artificial neural networks; Biological information theory; Biological system modeling; Computer science; Covariance matrix; Eigenvalues and eigenfunctions; Neural networks; Neurons; Parallel algorithms; Principal component analysis

Abstract

A form of ANN using interneurons has been shown to be capable of performing a principal component analysis of the input data. A review is given of the algorithm which does this. A model of the interneuron network with a set of more biologically feasible initial conditions has been developed-the weights to and from each interneuron are independent and yet the weights to and from the interneurons are shown to perform a PCA. A new parallel algorithm is proposed using the innate properties of the network. This is shown to converge in a completely parallel and homogeneous fashion to the principal components.

Arnumber

714231

Doi

10.1109/IJCNN.1993.714231

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Feature selection: a neural approach

Author

Castellano, G. and Fanelli, A.M.

Booktitle

{Neural Networks, 1999. IJCNN ‘99. International Joint Conference on}

Year

1999

Volume

5

Pages

3156–3160 vol.5

Keywords

conjugate gradient methods; feedforward neural nets; learning (artificial intelligence); least squares approximations; pattern classification; backward selection; classification problem; classification rate; feature selection; machine learning method; predictive accuracy; pruning; relative weight connections; relevant features; Accuracy; Artificial neural networks; Iterative algorithms; Learning systems; Linear systems; Machine learning algorithms; Neural networks; Pattern recognition; Statistics; Training data

Abstract

Feature selection is an integral part of most learning algorithms. By selecting relevant features of the data, higher predictive accuracy or classification rate can be expected from a machine learning method. We propose an approach to feature selection based on neural network pruning. The method performs a backward selection by successively removing input nodes in a network trained with the complete set of features as inputs. When an input node is removed, and relative weight connections are excised, the remaining weights are updated so as to keep approximately unchanged the behavior of the network. A simple criterion to select input nodes to be removed is developed. Experimental results over a well-known classification problem show the feasibility of the proposed approach and encourage its application to other classification tasks

Arnumber

836157

Doi

10.1109/IJCNN.1999.836157

Issn

1098-7576

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Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

Author

Nathans, Laura L. and Oswald, Frederick L. and Nimon, Kim

Journal

Practical Assessment, Research \& Evaluatino

Year

2012

Volume

17

Number

9

Histología del sistema nervioso del hombre y de los vertebrados

Publisher

A. Maloine

Year

1909

Author

y Cajal, S. Ramón

Booktitle

{Histologie du système nerveux de l’homme \& des vertébrés}

Location

Paris

The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain

Author

Rosenblatt, Frank

Journal

Psychological Review

Year

1958

Volume

65

Number

6

Pages

386–408

Keywords

deep networks perceptron rosenblatt

Added-At

2013-12-04T11:32:42.000+0100

Interhash

dc0cef9dc06033a04f525efdcde7a660

Intrahash

afcce29c2471aaa8480d2c2159361e88

Url

http://www.bibsonomy.org/bibtex/2afcce29c2471aaa8480d2c2159361e88/prlz77

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Bibsonomy

Adaptive switching circuits

Author

Widrow, B. and Hoff, M. E.

Journal

Institute of Radio Engineers, Western Electronics Show and Convention

Year

1960

Volume

Part 4

Pages

96–104

Keywords

imported

Added-At

2011-05-09T23:10:52.000+0200

Interhash

2ace34c5debd5abc08e714f8ff1030b3

Intrahash

7a7f37cf869049f7682749db9b75e0ac

Url

http://www.bibsonomy.org/bibtex/27a7f37cf869049f7682749db9b75e0ac/josephausterwei

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Bibsonomy

On the statistical efficiency of the LMS algorithm with nonstationary inputs.

Author

Widrow, Bernard and Walach, E.

Journal

IEEE Transactions on Information Theory

Year

1984

Volume

30

Number

2

Pages

211–221

Keywords

dblp

Added-At

2011-10-31T00:00:00.000+0100

Interhash

edd06ee062603cb1e6c984606203519f

Intrahash

914472690da35ffc08009dcfd9b92fb2

Url

http://dblp.uni-trier.de/db/journals/tit/tit30.html#WidrowW84; http://dx.doi.org/10.1109/TIT.1984.1056892; http://www.bibsonomy.org/bibtex/2914472690da35ffc08009dcfd9b92fb2/dblp

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Bibsonomy

Generalization and information storage in networks of adaline neurons

Author

Widrow, B.

Journal

Self-Organizing Systems

Year

1962

Pages

435–461

Editor

Yovits, M. C. and Jacobi, G. T. and Goldstein, G. D.

Location

Chicago

Organization

Spartan, Washington

Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences

Author

Werbos, P.

School

Harvard University

Year

1974

Keywords

nnets

Abstract

CCNLab BibTeX

Added-At

2008-09-16T23:39:07.000+0200

Biburl

http://www.bibsonomy.org/bibtex/2cb68e6abdbcd8c97e57fd86c75d45d4c/brian.mingus

Interhash

4165e2708a0468e89f8305f21ee2c711

Intrahash

cb68e6abdbcd8c97e57fd86c75d45d4c

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Learning representations by back-propagating errors

Author

Rumelhart, David E. and Hinton, Geoff E. and Wilson, R. J.

Journal

Nature

Year

1986

Volume

323

Pages

533–536

Keywords

imported

Added-At

2011-05-09T23:10:52.000+0200

Interhash

355bc28df4254f1205d36134f0489fc4

Intrahash

a0e6056aaed37cc0c98943bbf809f094

Url

http://www.bibsonomy.org/bibtex/2a0e6056aaed37cc0c98943bbf809f094/josephausterwei

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Bibsonomy

Towards a theory of reinforcement-learning connectionist systems

Author

Williams, R. J.

Institution

Northeastern University

Year

1988

Journal

Technical Report NU CCS,88,3

Location

Boston, MA,

School

College of Computer Science

Reinforcement Learning: An Introduction

Publisher

MIT Press

Year

1998

Author

Sutton, R. S. and Barto, A. G.

Address

Cambridge, MA

Keywords

juergen

Abstract

idsia

Added-At

2008-02-26T11:58:58.000+0100

Citeulike-Article-Id

2381881

Interhash

9369ad4b6ab9e5aa15fb456143e9a09d

Intrahash

f63c24b66dac20a62200228f0f861f99

Priority

2

Url

http://www.bibsonomy.org/bibtex/2f63c24b66dac20a62200228f0f861f99/schaul

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Bibsonomy

Neuronlike adaptive elements that can solve dificult learning control problems

Author

Barto, A. G. and Sutton, R. S. and Anderson, C. W.

Journal

IEEE Transactions on Systems, Man, and Cybernetics

Year

1983

Number

13

The truck backer-upper: An example of self learning in neural networks

Author

Nguyen, N. and Widrow, B.

Booktitle

{Proceedings of the International Joint Conference on Neural Networks}

Year

1989

Pages

357–363

Publisher

IEEE Press

Keywords

juergen

Added-At

2008-03-11T14:52:34.000+0100

Citeulike-Article-Id

2381433

Interhash

b77e0b735011727d9fac7b1c2e808b6c

Intrahash

e582537c6dd165f1efcf1455c06863f6

Priority

2

Url

http://www.bibsonomy.org/bibtex/2e582537c6dd165f1efcf1455c06863f6/idsia

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Bibsonomy

WilliamsZipser89

A Learning Algorithm for Continually Running Fully Recurrent Neural Networks

Author

Williams, R. J. and Zipser, D.

Journal

Neural Computation

Year

1989

Volume

1

Number

2

Pages

270–280

Keywords

nnets

Abstract

CCNLab BibTeX

Added-At

2008-09-16T23:39:07.000+0200

Biburl

http://www.bibsonomy.org/bibtex/26347d1b28cf4e10ad5e212e574045343/brian.mingus

Interhash

17f3915c54c21aa4faf9376aa019efe1

Intrahash

6347d1b28cf4e10ad5e212e574045343

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Bibsonomy

Encoding for computation: Recognizing brief dynamical patterns by exploiting effects of weak rhythms on action-potential timing

Author

Hopfield, J. J.

Journal

Proceedings of the National Academy of Sciences

Year

2004

Volume

101

Number

16

Pages

6255–6260

Keywords

nnets

Abstract

CCNLab BibTeX

Added-At

2008-09-16T23:39:07.000+0200

Biburl

http://www.bibsonomy.org/bibtex/286f2fef4da58d282f7995e3a191635a9/brian.mingus

Interhash

3ff50a57ead4cb24cada7d096f54ce26

Intrahash

86f2fef4da58d282f7995e3a191635a9

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Bibsonomy

Neural Networks and Physical Systems with Emergent Collective Computational Abilities

Author

Hopfield, J. J.

Journal

Proceedings of the National Academy of Sciences

Year

1982

Volume

79

Pages

2554–2558

Keywords

nnets

Abstract

CCNLab BibTeX

Added-At

2008-09-16T23:39:07.000+0200

Biburl

http://www.bibsonomy.org/bibtex/24041c6a004f343806a7e47c9cd7ccf59/brian.mingus

Interhash

cf60d9bad127184617e0ad10ae86b78b

Intrahash

4041c6a004f343806a7e47c9cd7ccf59

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Bibsonomy

Building high-level features using large scale unsupervised learning

Author

Le, Quoc V. and Monga, Rajat and Devin, Matthieu and Corrado, Greg and Chen, Kai and Ranzato, Marc’Aurelio and Dean, Jeffrey and Ng, Andrew Y.

Journal

CoRR

Year

2011

Volume

abs/1112.6209

Keywords

dblp

Added-At

2013-02-18T00:00:00.000+0100

Interhash

ff4c0a949152e72fd2ebfb8fb534a379

Intrahash

4b7bbb91cc932bd91003f605e2ebf6a1

Url

http://dblp.uni-trier.de/db/journals/corr/corr1112.html#abs-1112-6209; http://arxiv.org/abs/1112.6209; http://www.bibsonomy.org/bibtex/24b7bbb91cc932bd91003f605e2ebf6a1/dblp

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Bibsonomy

A Fast Learning Algorithm for Deep Belief Nets

Author

Hinton, G. E. and Osindero, S. and Teh, Y. W.

Journal

Neural Computation

Year

2006

Volume

18

Pages

1527–1554

Keywords

A Algorithm Belief Deep Fast Learning Nets for

Added-At

2013-12-08T17:38:38.000+0100

Biburl

http://www.bibsonomy.org/bibtex/2f29089f5341f89661c623dd73403e943/prlz77

Interhash

e20c213844c6c160f4bcc59cfcdc845d

Intrahash

f29089f5341f89661c623dd73403e943

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Bibsonomy

Learning Deep Architectures for AI.

Author

Bengio, Yoshua

Journal

Foundations and Trends in Machine Learning

Year

2009

Volume

2

Number

1

Pages

1–127

Keywords

dblp

Abstract

dblp

Added-At

2009-11-22T00:00:00.000+0100

Date

2009-11-22

Interhash

30174ec5e2667a039cdc30c5d359dc47

Intrahash

c282fc3956f5ae1b9618c1437c066dd4

Url

http://dblp.uni-trier.de/db/journals/ftml/ftml2.html#Bengio09; http://dx.doi.org/10.1561/2200000006; http://www.bibsonomy.org/bibtex/2c282fc3956f5ae1b9618c1437c066dd4/dblp

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Bibsonomy

Self-Organized Formation of Topologically Correct Feature Maps

Author

Kohonen, Teuvo

Journal

Biological Cybernetics

Year

1982

Volume

43

Pages

59–69

Keywords

dipl\_literatur related\_work som visualization

Added-At

2008-03-21T17:13:55.000+0100

Biburl

http://www.bibsonomy.org/bibtex/2b08749f1704199f95ddcda488d03ae40/danielt

Interhash

2a476d6ac197f6938cbbf3f2683df1b4

Intrahash

b08749f1704199f95ddcda488d03ae40

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Bibsonomy

Self-Organizing Maps

Publisher

Springer Berlin Heidelberg

Year

2001

Author

Kohonen, Teuvo

Address

Berlin, Heidelberg

Keywords

clustering som

Abstract

The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About 4000 research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real-world problems. Many fields of science have adopted the SOM as a standard analytical tool: in statistics, signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. The SOM solves difficult high-dimensional and nonlinear problems such as feature extraction and classification of images and acoustic patterns, adaptive control of robots, and equalization, demodulation, and error-tolerant transmission of signals in telecommunications. A new area is organization of very large document collections. Last but not least, it may be mentioned that the SOM is one of the most realistic models of the biological brain function. This new edition includes a survey of over 2000 contemporary studies to cover the newest results; case examples were provided with detailed formulae, illustrations, and tables; a new chapter on Software Tools for SOM was written, other chapters were extended or reorganized.

Added-At

2014-07-01T14:20:51.000+0200

Description

Self-Organizing Maps - Springer

Interhash

ccad3f96b0b87d57743319ef020caec9

Intrahash

fdca87747a451a608791fc234073f0e0

Isbn

9783642569272 3642569277

Url

http://link.springer.com/book/10.1007/978-3-642-56927-2; http://www.bibsonomy.org/bibtex/2fdca87747a451a608791fc234073f0e0/saos

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Bibsonomy

A Hybrid Parallel SOM Algorithm for Large Maps in Data-Mining

Author

Silva, Bruno and Marques, Nuno

Year

2007

Journal

Proceedings of 13th Portuguese Conference on Artificial Intelligence (EPIA 2007), Workshop on Business intelligence, Portugal. IEEE Guimares

Data Compression, Feature Extraction, and Autoassociation in Feedforward Neural Networks

Author

Oja, E.

Booktitle

{Artificial Neural Networks}

Year

1991

Editor

Kohonen, T. and Mäkisara, K. and Simula, O. and Kangas, J.

Volume

1

Pages

737–745

Publisher

Elsevier Science Publishers B.V., North-Holland

Keywords

juergen

Abstract

idsia

Added-At

2008-02-26T11:58:58.000+0100

Citeulike-Article-Id

2381436

Interhash

8d0494e7be1de57a0e19253f0b00cfd6

Intrahash

7dfc1445442c4b99dd8acdfc18cd3d06

Priority

2

Url

http://www.bibsonomy.org/bibtex/27dfc1445442c4b99dd8acdfc18cd3d06/schaul

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Bibsonomy

Locally Adaptive Metric Nearest-Neighbor Classification.

Author

Domeniconi, Carlotta and Peng, Jing and Gunopulos, Dimitrios

Journal

IEEE Trans. Pattern Anal. Mach. Intell.

Year

2002

Volume

24

Number

9

Pages

1281–1285

Keywords

dblp

Added-At

2011-11-07T00:00:00.000+0100

Interhash

f1942e577d70cc5ab32362697512c258

Intrahash

1f1573add38e60e3217f9aa389516efc

Url

http://dblp.uni-trier.de/db/journals/pami/pami24.html#DomeniconiPG02; http://doi.ieeecomputersociety.org/10.1109/TPAMI.2002.1033219; http://www.bibsonomy.org/bibtex/21f1573add38e60e3217f9aa389516efc/dblp

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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Author

Kohavi, Ron

Booktitle

{Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1995}

Year

1995

Pages

1137–1145

Keywords

QABook imported

Abstract

Daniel Sonntag all references

Added-At

2009-11-25T18:41:14.000+0100

Biburl

http://www.bibsonomy.org/bibtex/2d795ea815b2a2f738eabdfdcc5aa2df8/sonntag

Interhash

496571348acb09ebee18f3223e717a2f

Intrahash

d795ea815b2a2f738eabdfdcc5aa2df8

Owner

sonntag

Timestamp

2009.11.25

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Bibsonomy

Neural fraud detection in credit card operations.

Author

Dorronsoro, José R. and Ginel, Francisco and Sanchez, Carmen and Cruz, Carlos Santa

Journal

IEEE Transactions on Neural Networks

Year

1997

Volume

8

Number

4

Pages

827–834

Keywords

dblp

Added-At

2013-11-05T00:00:00.000+0100

Interhash

e3b33cf05680fc5d0990c1c1eb3ff85e

Intrahash

b78a18109721aef84462069806990d43

Url

http://dblp.uni-trier.de/db/journals/tnn/tnn8.html#DorronsoroGSC97; http://dx.doi.org/10.1109/72.595879; http://www.bibsonomy.org/bibtex/2b78a18109721aef84462069806990d43/dblp

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Bibsonomy

Data mining in soft computing framework: a survey.

Author

Mitra, Sushmita and Pal, Sankar K. and Mitra, Pabitra

Journal

IEEE Transactions on Neural Networks

Year

2002

Volume

13

Number

1

Pages

3–14

Keywords

dblp

Added-At

2012-05-04T00:00:00.000+0200

Interhash

5c05b470ce1f02ba892338569d90cd1f

Intrahash

2a7ce09f1500c41c8e7b557863afdb1e

Url

http://dblp.uni-trier.de/db/journals/tnn/tnn13.html#MitraPM02; http://dx.doi.org/10.1109/72.977258; http://www.bibsonomy.org/bibtex/22a7ce09f1500c41c8e7b557863afdb1e/dblp

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Bibsonomy

Independent component analysis: algorithms and applications

Author

Hyvärinen, A and Oja, E

Journal

Neural Networks: The Official Journal of the International Neural Network Society

Year

2000

Volume

13

Number

4-5

Pages

411–430

Month

Jun

Keywords

ica hebbian learning network neural

Abstract

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis {(ICA)} is a recently developed method in which the goal is to find a linear representation of {non-Gaussian} data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of {ICA,} and our recent work on the subject.

Added-At

2010-07-13T13:28:23.000+0200

Biburl

http://www.bibsonomy.org/bibtex/2470a9bd2d785259d08debabf0e89a1db/mhwombat

Doi

10.1016/S0893-6080(00)00026-5

File

:/home/amy/taighde/docs/neural\_nets/ICA\_\_NN00new.pdf:PDF

Groups

public

Interhash

2de559ac29677b060fba7b19f35d66e3

Intrahash

470a9bd2d785259d08debabf0e89a1db

Issn

0893-6080

Note

{PMID:} 10946390

Url

http://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf

Username

mhwombat

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Bibsonomy

Neural Networks, Principal Components, and Subspaces

Author

Oja, E.

Journal

International Journal of Neural Systems

Year

1989

Volume

1

Number

1

Pages

61–68

Keywords

juergen

Abstract

idsia

Added-At

2008-02-26T11:58:58.000+0100

Citeulike-Article-Id

2381437

Interhash

eb55d0661315a977d0b3dd576b9a96df

Intrahash

f39b19da5193c18bd64d8e3e9398fba2

Priority

2

Url

http://www.bibsonomy.org/bibtex/2f39b19da5193c18bd64d8e3e9398fba2/schaul

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Bibsonomy

Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition.

Author

Liu, Cheng-Lin and Nakagawa, Masaki

Journal

Pattern Recognition

Year

2001

Volume

34

Number

3

Pages

601–615

Keywords

dblp

Abstract

dblp

Added-At

2004-02-20T00:00:00.000+0100

Date

2004-02-20

Interhash

8f8cb80dcc8ca8a42dc5450af7e2f92b

Intrahash

681aeac6bbcad66d00eebd8b29b498d3

Url

http://dblp.uni-trier.de/db/journals/pr/pr34.html#LiuN01; http://dx.doi.org/10.1016/S0031-3203(00)00018-2; http://www.bibsonomy.org/bibtex/2681aeac6bbcad66d00eebd8b29b498d3/dblp

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Bibsonomy

Automatic Cluster Detection in Kohonen’s SOM

Author

Brugger, D. and Bogdan, M. and Rosenstiel, W.

Journal

Neural Networks, IEEE Transactions on

Year

2008

Volume

19

Number

3

Pages

442–459

Month

March

Keywords

data analysis; data visualisation; pattern clustering; self-organising feature maps; Clusot algorithm; Kohonen self-organizing map; automatic cluster detection; data visualization; explorative data analysis; n-dimensional grid topology; Clustering methods; exploratory data analysis; neural network architecture; prosthetics; self-organizing feature maps; Algorithms; Decision Trees; Humans; Information Storage and Retrieval; Neural Networks (Computer); Signal Processing; Computer-Assisted

Abstract

Kohonen’s self-organizing map (SOM) is a popular neural network architecture for solving problems in the field of explorative data analysis, clustering, and data visualization. One of the major drawbacks of the SOM algorithm is the difficulty for nonexpert users to interpret the information contained in a trained SOM. In this paper, this problem is addressed by introducing an enhanced version of the Clusot algorithm. This algorithm consists of two main steps: 1) the computation of the Clusot surface utilizing the information contained in a trained SOM and 2) the automatic detection of clusters in this surface. In the Clusot surface, clusters present in the underlying SOM are indicated by the local maxima of the surface. For SOMs with 2-D topology, the Clusot surface can, therefore, be considered as a convenient visualization technique. Yet, the presented approach is not restricted to a certain type of 2-D SOM topology and it is also applicable for SOMs having an n-dimensional grid topology.

Arnumber

4436179

Doi

10.1109/TNN.2007.909556

Issn

1045-9227

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IEEEXplore

CRISP-DM 1.0 Step-by-step data mining guide

Author

Chapman, Pete and Clinton, Julian and Kerber, Randy and Khabaza, Thomas and Reinartz, Thomas and Shearer, Colin and Wirth, Rudiger

Institution

The CRISP-DM consortium

Year

2000

Month

August

Keywords

CRISP\_DM

Abstract

CRISP DM reference

Added-At

2009-09-30T15:27:52.000+0200

Citeulike-Article-Id

1025172

Citeulike-Linkout-0

http://www.crisp-dm.org/CRISPWP-0800.pdf

Interhash

d20b2a4a6204b04306c3fa3fcaf8ddbf

Intrahash

040740579b3d449a8d2b38efbd64b0bd

Posted-At

2007-01-04 16:36:46

Priority

2

Url

http://www.crisp-dm.org/CRISPWP-0800.pdf; http://www.bibsonomy.org/bibtex/2040740579b3d449a8d2b38efbd64b0bd/andrea.zanda

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Deep Learning in Neural Networks: An Overview

Author

Schmidhuber, Jürgen

Institution

IDSIA-03-14

Year

2014

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