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Artificial Neural Networks in Pattern Recognition

Third IAPR TC3 Workshop, ANNPR 2008 Paris, France, July 2-4, 2008, Proceedings

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Lionel Prevost
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  2. Simone Marinai
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  3. Friedhelm Schwenker
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5064)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ANNPR 2008.

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Table of contents (29 papers)

  1. Front Matter

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  2. Unsupervised Learning

    1. Patch Relational Neural Gas – Clustering of Huge Dissimilarity Datasets

      • Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
      Pages 1-12

    2. The Block Generative Topographic Mapping

      • Rodolphe Priam, Mohamed Nadif, Gérard Govaert
      Pages 13-23

    3. Kernel k-Means Clustering Applied to Vector Space Embeddings of Graphs

      • Kaspar Riesen, Horst Bunke
      Pages 24-35

    4. Probabilistic Models Based on the Π-Sigmoid Distribution

      • Anastasios Alivanoglou, Aristidis Likas
      Pages 36-43

    5. How Robust Is a Probabilistic Neural VLSI System Against Environmental Noise

      • C. C. Lu, C. C. Li, H. Chen
      Pages 44-53

  3. Supervised Learning

    1. Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis

      • Shigeo Abe
      Pages 54-65

    2. Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network

      • Jean-François Connolly, Eric Granger, Robert Sabourin
      Pages 66-77

    3. Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics

      • M. Strickert, P. Schneider, J. Keilwagen, T. Villmann, M. Biehl, B. Hammer
      Pages 78-89

    4. Neural Approximation of Monte Carlo Policy Evaluation Deployed in Connect Four

      • Stefan Faußer, Friedhelm Schwenker
      Pages 90-100

    5. Cyclostationary Neural Networks for Air Pollutant Concentration Prediction

      • Monica Bianchini, Ernesto Di Iorio, Marco Maggini, Augusto Pucci
      Pages 101-112

    6. Fuzzy Evolutionary Probabilistic Neural Networks

      • V. L. Georgiou, Ph. D. Alevizos, M. N. Vrahatis
      Pages 113-124

    7. Experiments with Supervised Fuzzy LVQ

      • Christian Thiel, Britta Sonntag, Friedhelm Schwenker
      Pages 125-132

    8. A Neural Network Approach to Similarity Learning

      • Stefano Melacci, Lorenzo Sarti, Marco Maggini, Monica Bianchini
      Pages 133-136

    9. Partial Discriminative Training of Neural Networks for Classification of Overlapping Classes

      • Cheng-Lin Liu
      Pages 137-146

  4. Multiple Classifiers

    1. Boosting Threshold Classifiers for High– Dimensional Data in Functional Genomics

      • Ludwig Lausser, Malte Buchholz, Hans A. Kestler
      Pages 147-156

    2. Decision Fusion on Boosting Ensembles

      • Joaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo
      Pages 157-167

    3. The Mixture of Neural Networks as Ensemble Combiner

      • Mercedes Fernández-Redondo, Joaquín Torres-Sospedra, Carlos Hernández-Espinosa
      Pages 168-179

    4. Combining Methods for Dynamic Multiple Classifier Systems

      • Amber Tomas
      Pages 180-192

    5. Researching on Multi-net Systems Based on Stacked Generalization

      • Carlos Hernández-Espinosa, Joaquín Torres-Sospedra, Mercedes Fernández-Redondo
      Pages 193-204
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