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Month
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5
Read
1
Author
Yan, Weizhong
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IEEE transactions on neural networks and learning systems
Year
2012
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Number
7
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Month
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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. 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
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
X-Fetchedfrom
Bibsonomy
Author
Cheeseman, Peter
Booktitle
{IJCAI}
Year
1985
Publisher
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
X-Fetchedfrom
Bibsonomy
Author
Cauwenberghs, Gert
Booktitle
{NIPS}
Year
1993
Publisher
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
X-Fetchedfrom
Bibsonomy
Author
Banko, Michele and Brill, Eric
Booktitle
{ACL}
Year
2001
Pages
26-33
Publisher
Morgan Kaufmann Publishers
Keywords
dblp
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1989
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Touretzky, David S.
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Flanagan, John A. and Hasler, Martin
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1995
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Mira, José and Hernández, Francisco Sandoval
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930
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322-329
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{Lecture Notes in Computer Science}
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Springer
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Crossref
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2001
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2011
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June
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Institute, McKinsey Global
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McKinsey Global Institute
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Krzyzak, Adam and Xu, Ley
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1996
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ICONIP'96
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Hong Kong
Author
Garcia, Emilia
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Boletic 65
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2013
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April
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Yiu, Chris
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Police Exchange
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2012
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Exchange, Police
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978-1-907689-22-2
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Gantz, John and Reintzel, David
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IDC
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2012
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December
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2013
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September
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CoRR
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2012
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November
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2013
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June
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Innovation edge
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BBVA
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Mitchell, Tom
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2006
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CMU ML-06 108
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machine-learning mitchell
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Hand, David J.
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SIGKDD Explorations
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1999
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1
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1
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16-19
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dblp
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Cdrom
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Turing, Alan M.
Journal
Mind
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1950
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59
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433-460
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Fayyad, U. and Piatetsky-Shapiro, G. and Smyth, P.
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AI Magazine
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1996
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37-54
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imported
Abstract
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Prentice Hall
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2010
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Russell, Stuart and Norvig, Peter
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3
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ai
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MIT Press
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1985
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Haugeland, John
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AT15
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Boyd
Fraser Pub. Co.
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1978
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Bellman, Richard
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Addison Wesley
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1985
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Charniak, Eugene and McDermott, Drew
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2000 book nlp
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Addison-Wesley
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1992
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Winston, Patrick Henry
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dblp
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MIT Press
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1992
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Kurzweil, Raymond
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dblp
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McGraw-Hill
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1991
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Rich, Elaine and Knight, Kevin
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dblp
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Oxford University Press
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1998
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Poole, David and Mackworth, Alan K. and Goebel, Randy
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Morgan Kaufmann Publishers
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1998
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Nilsson, Nils J.
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San Mateo, CA
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Bibsonomy
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McCulloch, W. S. and Pitts, W.
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Bulletin of Mathematical Biophysics
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1943
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5
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115-133
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nnets
Abstract
CCNLab BibTeX
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Bibsonomy
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John Wiley
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1949
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Hebb, Donald O.
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New York
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imported
Abstract
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Bibsonomy
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MIT Press
Year
1969
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Minsky, M. and Papert, S.
Address
Cambridge, MA
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critic minsky papert perceptron
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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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Bibsonomy
Author
Lindsay, Robert K. and Buchanan, Bruce G. and Feigenbaum, Edward A. and Lederberg, Joshua
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Artif. Intell.
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1993
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61
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2
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209-261
Keywords
dblp
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McDermott, John
Journal
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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Bibsonomy
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Blaisdell
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1969
Author
Bryson, A. E. and Ho, Y. C.
Address
New York
Keywords
nnets
Abstract
CCNLab BibTeX
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Biburl
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Bibsonomy
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MIT Press
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1986
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Rumelhart, D.E. and McClelland, J.L.
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I+II
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imported
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Bibsonomy
Author
Baum, Leonard E. and Petrie, Ted
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
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Bibsonomy
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Baum, Leonard E. and Eagon, J. A.
Journal
Bulletin of the American Mathematical Society
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1967
Volume
73
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3
Pages
360-363
Month
May
Keywords
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Publisher
Morgan Kaufmann
Year
1988
Author
Pearl, J.
Keywords
imported
Added-At
2008-10-07T16:03:39.000+0200
Interhash
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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
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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
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X-Fetchedfrom
Bibsonomy
Publisher
Springer
Year
2007
Publisher
Goertzel, Ben and Pennachin, Cassio
Series
{Cognitive Technologies}
Keywords
dblp
Added-At
2013-09-10T00:00:00.000+0200
Booktitle
{Artificial General Intelligence}
Interhash
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Intrahash
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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
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X-Fetchedfrom
Bibsonomy
Author
Yudkowsky, E.
Journal
Oxford University Press
Year
2008
Author
Omohundro, S.
Year
2008
Journal
Appeared in AGI-08 - Proceedings of the First Conference on Artificial General Intelligence
OLeary13
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). data, and it has been used to analyze big data for key insights).
Arnumber
6547979
Doi
10.1109/MIS.2013.39
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1541-1672
X-Fetchedfrom
IEEEXplore
Author
Cox, M. and Ellsworth, D.
Year
1997
Pages
5-1-5-17
Publisher
ACM Siggraph
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
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9780071808187
Url
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X-Fetchedfrom
Bibsonomy
Author
Dean, Jeffrey and Ghemawat, Sanjay
Booktitle
{In OSDI{\rq}04: Proceedings of the 6th conference on Symposium on Opearting Systems Design {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
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Bibsonomy
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
analog 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.
(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.
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.
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.<>.
Arnumber
118697
Doi
10.1109/IJCNN.1989.118697
X-Fetchedfrom
IEEEXplore
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
X-Fetchedfrom
Bibsonomy
Author
Dervojeda, Kristina and Verzijl, Diederik and Nagtegaal, Fabian and Lengton, Mark and Rouwmaat, Elco
Month
September
Year
2013
Institution
EU
Organization
Business Innovation Observatory
Author
Vesset, Dan and Nadkarni, Ashish and Brothers, Rob and Christiansen, Christian A. and Conway, Steve and others
Year
2013
Month
December
Organization
IDC
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
X-Fetchedfrom
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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 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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Bibsonomy
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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Bibsonomy
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 Commission
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
organizational 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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IEEEXplore
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
X-Fetchedfrom
Bibsonomy
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
X-Fetchedfrom
Bibsonomy
Author
Platt, John C.
Journal
Advances in Neural Information Processing Systems
Year
1991
Volume
3
Pages
715-720
Publisher
Touretzky, D. S.
Publisher
Morgan Kaufmann, San Mateo
Author
Park, J. and Sandeberg, I. W.
Journal
Neural Computation
Year
1993
Volume
5
Pages
305-316
Author
Poggio, T. and Girosi, F.
Journal
Proceedings of IEEE
Year
1990
Volume
78
Pages
1481-1497
Author
Hornik, K. and Stinchcombe, M. and White, H.
Journal
Neural Networks
Year
1989
Volume
2
Pages
359-366
Author
Barron, A. R.
Journal
IEEE Transactions on Information Theory
Year
1993
Volume
39
Pages
930-944
Author
Kadirkamanathan, V. and Niranjan, M. and Fallside, F.
Journal
Advances in Neural Information Processing Systems
Year
1991
Volume
3
Pages
721-727
Publisher
Touretzky, D. S.
Publisher
Morgan Kaufman, San Mateo
Author
Platt, John
Journal
Neural Computation
Year
1991
Volume
3
Number
2
Pages
213-225
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
X-Fetchedfrom
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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
X-Fetchedfrom
Bibsonomy
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
X-Fetchedfrom
Bibsonomy
Author
Wu, Jiunn-Lin and Li, I-Jing
Journal
International Journal of Computer, Consumer and Control (IJ3C)
Year
2014
Volume
3
Number
1
Author
Yuan, Jen-Lun and Fine, T.L.
Journal
Neural Networks, IEEE Transactions on
Year
1998
Volume
9
Number
2
Pages
266-280
Month
Sea
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 overall performance. 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. 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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IEEEXplore
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
Author
Oja, Erkki
Journal
Neural Networks
Year
1992
Volume
5
Pages
927-935
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 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. 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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IEEEXplore
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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IEEEXplore
Author
Nathans, Laura L. and Oswald, Frederick L. and Nimon, Kim
Journal
Practical Assessment, Research
Evaluatino
Year
2012
Volume
17
Number
9
Publisher
A. Maloine
Year
1909
Author
y Cajal, S. Ramón
Booktitle
{Histology of the nervous system of man {Histologie du système nerveux de l'homme
des vertébrés}
Location
Paris
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
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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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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Author
Widrow, B.
Journal
Self-Organizing Systems
Year
1962
Pages
435-461
Publisher
Yovits, M. C. and Jacobi, G. T. and Goldstein, G. D.
Location
Chicago
Organization
Spartan, Washington
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
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Intrahash
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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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Author
Williams, R. J.
Institution
Northeastern University
Year
1988
Journal
Technical Report NU CCS,88,3
Location
Boston, MA,
School
College of Computer Science
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
X-Fetchedfrom
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Author
Barto, A. G. and Sutton, R. S. and Anderson, C. W.
Journal
IEEE Transactions on Systems, Man, and Cybernetics
Year
1983
Number
13
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
X-Fetchedfrom
Bibsonomy
WilliamsZipser89
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
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X-Fetchedfrom
Bibsonomy
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
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X-Fetchedfrom
Bibsonomy
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
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Bibsonomy
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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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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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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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
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X-Fetchedfrom
Bibsonomy
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-world problems.
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, 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.
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
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ccad3f96b0b87d57743319ef020caec9
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Isbn
9783642569272 3642569277
Url
http://link.springer.com/book/10.1007/978-3-642-56927-2
http://www.bibsonomy.org/bibtex/2fdca87747a451a608791fc234073f0e0/saos
X-Fetchedfrom
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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
Author
Oja, E.
Booktitle
{Artificial Neural Networks}
Year
1991
Publisher
Kohonen, T. and Mäkisara, K. and Simula, O. and Kangas, J.
Volume
1
Pages
737-745
Publisher
Elsevier Science Publishers B.V., North-Holland, Germany.
Keywords
juergen
Abstract
idsia
Added-At
2008-02-26T11:58:58.000+0100
Citeulike-Article-Id
2381436
Interhash
8d0494e7be1de57a0e19253f0b00cfd6
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Priority
2
Url
http://www.bibsonomy.org/bibtex/27dfc1445442c4b99dd8acdfc18cd3d06/schaul
X-Fetchedfrom
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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
X-Fetchedfrom
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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
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Intrahash
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Owner
sonntag
Timestamp
2009.11.25
X-Fetchedfrom
Bibsonomy
Author
Dorronsoro, José R. and Ginel, Francisco and Sanchez, Carmen and Cruz, Carlos Santa, 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
X-Fetchedfrom
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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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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. 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, 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 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_N00new.pdf:PDF
Groups
public
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2de559ac29677b060fba7b19f35d66e3
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Issn
0893-6080
Note
{PMID:} 10946390
Url
http://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf
Username
mhwombat
X-Fetchedfrom
Bibsonomy
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
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2
Url
http://www.bibsonomy.org/bibtex/2f39b19da5193c18bd64d8e3e9398fba2/schaul
X-Fetchedfrom
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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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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-organizing 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
X-Fetchedfrom
IEEEXplore
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
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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
X-Fetchedfrom
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Author
Schmidhuber, Jürgen
Institution
IDSIA-03-14
Year
2014
