Artificial Neural Networks in Pattern Recognition

1. Front Matter

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2. Invited Paper

   1. Large Margin Distribution Learning

      • Zhi-Hua Zhou

      Pages 1-11

3. Learning Algorithms and Architectures

   1. A Decorrelation Approach for Pruning of Multilayer Perceptron Networks

      • Hazem M. Abbas

      Pages 12-22

   2. Unsupervised Active Learning of CRF Model for Cross-Lingual Named Entity Recognition

      • Mohamed Farouk Abdel Hady, Abubakrelsedik Karali, Eslam Kamal, Rania Ibrahim

      Pages 23-34

   3. Incremental Feature Selection by Block Addition and Block Deletion Using Least Squares SVRs

      • Shigeo Abe

      Pages 35-46

   4. Low-Dimensional Data Representation in Data Analysis

      • Alexander Bernstein, Alexander Kuleshov

      Pages 47-58

   5. Analyzing Dynamic Ensemble Selection Techniques Using Dissimilarity Analysis

      • Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti

      Pages 59-70

   6. Hidden Markov Models Based on Generalized Dirichlet Mixtures for Proportional Data Modeling

      • Elise Epaillard, Nizar Bouguila

      Pages 71-82

   7. Majority-Class Aware Support Vector Domain Oversampling for Imbalanced Classification Problems

      • Markus Kächele, Patrick Thiam, Günther Palm, Friedhelm Schwenker

      Pages 83-92

   8. Forward and Backward Forecasting Ensembles for the Estimation of Time Series Missing Data

      • Tawfik A. Moahmed, Neamat El Gayar, Amir F. Atiya

      Pages 93-104

   9. Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streams

      • Christophe Pagano, Eric Granger, Robert Sabourin, Gian Luca Marcialis, Fabio Roli

      Pages 105-116

   10. Combining Bipartite Graph Matching and Beam Search for Graph Edit Distance Approximation

       • Kaspar Riesen, Andreas Fischer, Horst Bunke

       Pages 117-128

   11. Computing Upper and Lower Bounds of Graph Edit Distance in Cubic Time

       • Kaspar Riesen, Andreas Fischer, Horst Bunke

       Pages 129-140

   12. Linear Contrast Classifiers in High-Dimensional Spaces

       • Florian Schmid, Ludwig Lausser, Hans A. Kestler

       Pages 141-152

   13. A New Multi-class Fuzzy Support Vector Machine Algorithm

       • Friedhelm Schwenker, Markus Frey, Michael Glodek, Markus Kächele, Sascha Meudt, Martin Schels et al.

       Pages 153-164

   14. A Reinforcement Learning Algorithm to Train a Tetris Playing Agent

       • Patrick Thiam, Viktor Kessler, Friedhelm Schwenker

       Pages 165-170

   15. Bio-Inspired Optic Flow from Event-Based Neuromorphic Sensor Input

       • Stephan Tschechne, Roman Sailer, Heiko Neumann

       Pages 171-182

4. Applications

   1. Prediction of Insertion-Site Preferences of Transposons Using Support Vector Machines and Artificial Neural Networks

      • Maryam Ayat, Michael Domaratzki

      Pages 183-192

   2. Automatic Bridge Crack Detection – A Texture Analysis-Based Approach

      • Sukalpa Chanda, Guoping Bu, Hong Guan, Jun Jo, Umapada Pal, Yew-Chaye Loo et al.

      Pages 193-203

   3. Part-Based High Accuracy Recognition of Serial Numbers in Bank Notes

      • Bo-Yuan Feng, Mingwu Ren, Xu-Yao Zhang, Ching Y. Suen

      Pages 204-215

This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.

• classification
• feature selection
• information extraction
• kernel methods
• learning algorithms
• machine learning
• meta-learning
• multiple classifier systems
• neural networks
• optimal network architecture
• pattern classification
• statistical learning
• support vector machines
• time series prediction
• unsupervised active learning

• Faculty of Computers and Information, Orman, Cairo University, Giza, Egypt

  Neamat Gayar

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

  Friedhelm Schwenker

• Department of Computer Science and Software Engineering, Concordia University, Monral, Canada

  Cheng Suen

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