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Home > Artificial Neural Networks - ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I > Relevance Metrics to Reduce Input Dimensions in Artificial Neural Networks
Artificial Neural Networks - ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I |
Joaquim Marques de Sá; Luis A. Alexandre; Wlodzislaw Duch; Danilo Mandic
Springer Berlin Heidelberg
Artificial Neural Networks - ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I
Relevance Metrics to Reduce Input Dimensions in Artificial Neural Networks
Authors

Héctor F. Satizábal M.; Andres Pérez-Uribe

ARI Id

1664723158129_2931846

Access

Not Available Free

Pages

39-48

DOI

10.1007/978-3-540-74690-4_5

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-540-74690-4_5

Table of Contents of Book
Showing 1 to 20 of 100 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
Generalization Error of Automatic Relevance Determination
Shinichi Nakajima; Sumio Watanabe
1-10
On a Singular Point to Contribute to a Learning Coefficient and Weighted Resolution of Singularities
Takeshi Matsuda; Sumio Watanabe
11-18
Improving the Prediction Accuracy of Echo State Neural Networks by Anti-Oja’s Learning
Štefan Babinec; Jiří Pospíchal
19-28
Theoretical Analysis of Accuracy of Gaussian Belief Propagation
Yu Nishiyama; Sumio Watanabe
29-38
Relevance Metrics to Reduce Input Dimensions in Artificial Neural Networks
Héctor F. Satizábal M.; Andres Pérez-Uribe
39-48
An Improved Greedy Bayesian Network Learning Algorithm on Limited Data
Feng Liu; Fengzhan Tian; Qiliang Zhu
49-57
Incremental One-Class Learning with Bounded Computational Complexity
Rowland R. Sillito; Robert B. Fisher
58-67
Estimating the Size of Neural Networks from the Number of Available Training Data
Georgios Lappas
68-77
A Maximum Weighted Likelihood Approach to Simultaneous Model Selection and Feature Weighting in Gaussian Mixture
Yiu-ming Cheung; Hong Zeng
78-87
Estimation of Poles of Zeta Function in Learning Theory Using Padé Approximation
Ryosuke Iriguchi; Sumio Watanabe
88-97
Neural Network Ensemble Training by Sequential Interaction
M. A. H. Akhand; Kazuyuki Murase
98-108
Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification
Daniel Schneegaß; Steffen Udluft; Thomas Martinetz
109-118
Structure Learning with Nonparametric Decomposable Models
Anton Schwaighofer; Mathäus Dejori; Volker Tresp; Martin Stetter
119-128
Recurrent Bayesian Reasoning in Probabilistic Neural Networks
Jiří Grim; Jan Hora
129-138
Resilient Approximation of Kernel Classifiers
Thorsten Suttorp; Christian Igel
139-148
Incremental Learning of Spatio-temporal Patterns with Model Selection
Koichiro Yamauchi; Masayoshi Sato
149-158
Accelerating Kernel Perceptron Learning
Daniel García; Ana González; José R. Dorronsoro
159-168
Analysis and Comparative Study of Source Separation Performances in Feed-Forward and Feed-Back BSSs Based on Propagation Delays in Convolutive Mixture
Akihide Horita; Kenji Nakayama; Akihiro Hirano
169-179
Learning Highly Non-separable Boolean Functions Using Constructive Feedforward Neural Network
Marek Grochowski; Włodzisław Duch
180-189
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 100 entries
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