Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science
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104

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Published By Springer Berlin Heidelberg

9783642042737, 9783642042744

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A Simple Proof of the Convergence of the SMO Algorithm for Linearly Separable Problems

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_93 ◽

2009 ◽

pp. 904-912 ◽

Cited By ~ 4

Author(s):

Jorge López ◽

José R. Dorronsoro

Keyword(s):

Simple Proof ◽

Separable Problems ◽

Smo Algorithm

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Height Defuzzification Method on L ∞ Space

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_62 ◽

2009 ◽

pp. 598-607

Author(s):

Takashi Mitsuishi ◽

Yasunari Shidama

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Learning Complex Population-Coded Sequences

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_31 ◽

2009 ◽

pp. 296-305 ◽

Cited By ~ 2

Author(s):

Kiran V. Byadarhaly ◽

Mithun Perdoor ◽

Suresh Vasa ◽

Emmanuel Fernandez ◽

Ali A. Minai

Keyword(s):

Complex Population

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Minimization of Quadratic Binary Functional with Additive Connection Matrix

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_17 ◽

2009 ◽

pp. 161-170

Author(s):

Leonid Litinskii

Keyword(s):

Connection Matrix

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Almost Random Projection Machine

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_81 ◽

2009 ◽

pp. 789-798 ◽

Cited By ~ 6

Author(s):

Włodzisław Duch ◽

Tomasz Maszczyk

Keyword(s):

Random Projection

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Hierarchical Architecture with Modular Network SOM and Modular Reinforcement Learning

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_57 ◽

2009 ◽

pp. 546-556

Author(s):

Masumi Ishikawa ◽

Kosuke Ueno

Keyword(s):

Reinforcement Learning ◽

Hierarchical Architecture ◽

Modular Network

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Active Generation of Training Examples in Meta-Regression

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_4 ◽

2009 ◽

pp. 30-39 ◽

Cited By ~ 2

Author(s):

Ricardo B. C. Prudêncio ◽

Teresa B. Ludermir

Keyword(s):

Meta Regression ◽

Training Examples ◽

Active Generation

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Identifying Clusters Using Growing Neural Gas: First Results

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_56 ◽

2009 ◽

pp. 536-545

Author(s):

Riccardo Rizzo ◽

Alfonso Urso

Keyword(s):

Growing Neural Gas ◽

Neural Gas ◽

First Results

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An Automatic Parameter Adjustment Method of Pulse Coupled Neural Network for Image Segmentation

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_86 ◽

2009 ◽

pp. 834-843 ◽

Cited By ~ 10

Author(s):

Masato Yonekawa ◽

Hiroaki Kurokawa

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Parameter Adjustment ◽

Adjustment Method ◽

Pulse Coupled Neural Network

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The GMM-SVM Supervector Approach for the Recognition of the Emotional Status from Speech

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04274-4_92 ◽

2009 ◽

pp. 894-903 ◽

Cited By ~ 12

Author(s):

Friedhelm Schwenker ◽

Stefan Scherer ◽

Yasmine M. Magdi ◽

Günther Palm

Keyword(s):

Emotional Status

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Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

A Simple Proof of the Convergence of the SMO Algorithm for Linearly Separable Problems

Height Defuzzification Method on L ∞ Space

Learning Complex Population-Coded Sequences

Minimization of Quadratic Binary Functional with Additive Connection Matrix

Almost Random Projection Machine

Hierarchical Architecture with Modular Network SOM and Modular Reinforcement Learning

Active Generation of Training Examples in Meta-Regression

Identifying Clusters Using Growing Neural Gas: First Results

An Automatic Parameter Adjustment Method of Pulse Coupled Neural Network for Image Segmentation

The GMM-SVM Supervector Approach for the Recognition of the Emotional Status from Speech

Artificial Neural Networks – ICANN 2009 - Lecture Notes in Computer Science
Latest Publications