Artificial Neural Networks in Pattern Recognition

1. Front Matter

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2. Learning Algorithms

   1. How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?

      • Bassam Mokbel, Sebastian Gross, Markus Lux, Niels Pinkwart, Barbara Hammer

      Pages 1-13

   2. Kernel Robust Soft Learning Vector Quantization

      • Daniela Hofmann, Barbara Hammer

      Pages 14-23

   3. Incremental Learning by Message Passing in Hierarchical Temporal Memory

      • Davide Maltoni, Erik M. Rehn

      Pages 24-35

   4. Representative Prototype Sets for Data Characterization and Classification

      • Ludwig Lausser, Christoph Müssel, Hans A. Kestler

      Pages 36-47

   5. Feature Selection by Block Addition and Block Deletion

      • Takashi Nagatani, Shigeo Abe

      Pages 48-59

   6. Gradient Algorithms for Exploration/Exploitation Trade-Offs: Global and Local Variants

      • Michel Tokic, Günther Palm

      Pages 60-71

   7. Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution to the Problem of Parameter Learning

      • Edmondo Trentin, Marco Bongini

      Pages 72-81

   8. Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Structure Learning and an Empirical Evaluation

      • Marco Bongini, Edmondo Trentin

      Pages 82-92

   9. Statistical Recognition of a Set of Patterns Using Novel Probability Neural Network

      • Andrey V. Savchenko

      Pages 93-103

   10. On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognition

       • Miriam Schmidt, Günther Palm, Friedhelm Schwenker

       Pages 104-114

3. Applications

   1. Improving Iris Recognition through New Target Vectors in MLP Artificial Neural Networks

      • José Ricardo Gonçalves Manzan, Shigueo Nomura, Keiji Yamanaka, Milena Bueno Pereira Carneiro, Antônio C. Paschoarelli Veiga

      Pages 115-126

   2. Robustness of a CAD System on Digitized Mammograms

      • Antonio García-Manso, Carlos J. García-Orellana, Ramón Gallardo-Caballero, Nico Lanconelli, Horacio González-Velasco, Miguel Macías-Macías

      Pages 127-138

   3. Facial Expression Recognition Using Game Theory

      • Kaushik Roy, Mohamed S. Kamel

      Pages 139-150

   4. Classification of Segmented Objects through a Multi-net Approach

      • Alessandro Zamberletti, Ignazio Gallo, Simone Albertini, Marco Vanetti, Angelo Nodari

      Pages 151-162

   5. A Decision Support System for the Prediction of the Trabecular Fracture Zone

      • Vasileios Korfiatis, Simone Tassani, George K. Matsopoulos

      Pages 163-174

   6. Teeth/Palate and Interdental Segmentation Using Artificial Neural Networks

      • Kelwin Fernandez, Carolina Chang

      Pages 175-185

   7. On Instance Selection in Audio Based Emotion Recognition

      • Sascha Meudt, Friedhelm Schwenker

      Pages 186-192

   8. Traffic Sign Classifier Adaption by Semi-supervised Co-training

      • Matthias Hillebrand, Ulrich Kreßel, Christian Wöhler, Franz Kummert

      Pages 193-200

   9. Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election

      • Paul T. Pearson, Cameron I. Cooper

      Pages 201-212

This book constitutes the refereed proceedings of the 5th INNS IAPR TC3 GIRPR International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 revised full papers presented were carefully reviewed and selected for inclusion in this volume. They cover a large range of topics in the field of neural network- and machine learning-based pattern recognition presenting and discussing the latest research, results, and ideas in these areas.

• classification
• learning algorithms
• neural networks
• sequential data
• unsupervised machine learning

• Fondazione Bruno Kessler (FBK), Trento, Italy

  Nadia Mana

• Institute of Neural Information Processing, University of Ulm, Ulm, Germany

  Friedhelm Schwenker

• Dipartimento di Ingegneria dell’Informazione, Università di Siena, Siena, Italy

  Edmondo Trentin

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