Computer Recognition Systems

1. Front Matter

   Pages I-XVIII

   PDF

2. Invited Papers

   1. Front Matter

      Pages 1-1

      PDF

   2. Neural Network-Based Chaotic Pattern Recognition — Part 2: Stability and Algorithmic Issues

      • D. Calitoiu, John B. Oommen, D. Nussbaum

      Pages 3-16

   3. A Brief Survey of Dynamic Texture Description and Recognition

      • Dmitry Chetverikov, Renaud Péteri

      Pages 17-26

   4. Open Issues in Pattern Recognition

      • Robert P. W. Duin, Elżbieta Pekalska

      Pages 27-42

   5. The Role of Ontological Models in Pattern Recognition

      • Juliusz L. Kulikowski

      Pages 43-52

   6. Current Feature Selection Techniques in Statistical Pattern Recognition

      • Pavel Pudil, Petr Somol

      Pages 53-68

3. Features, Learning and Classifiers

   1. Front Matter

      Pages 69-69

      PDF

   2. Margin-based Diversity Measures for Ensemble Classifiers

      • Tomasz Arodź

      Pages 71-78

   3. Boosting the Fisher Linear Discriminant with Random Feature Subsets

      • Tomasz Arodź

      Pages 79-86

   4. A Look-Ahead Branch and Bound Pruning Scheme for Trie-Based Approximate String Matching

      • Ghada Badr, John B. Oommen

      Pages 87-94

   5. Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter

      • Jacek Biesiada, Włodzisław Duch

      Pages 95-103

   6. Linear Ranked Regression - Designing Principles

      • Leon Bobrowski

      Pages 105-112

   7. Time Series Patterns Recognition with Genetic Algorithms

      • Marcin Borkowski

      Pages 113-120

   8. Selection of Fuzzy-Valued Loss Function in Two Stage Binary Classifier

      • Robert Burduk

      Pages 121-128

   9. A New Rejection Strategy for Convolutional Neural Network by Adaptive Topology

      • Hubert Cecotti, Abdel Belaïd

      Pages 129-136

   10. Fast PCA and LDA for JPEG Images

       • Weilong Chen, Meng Joo Er, Shiqian Wu

       Pages 137-144

   11. A Hybrid ε-Insensitive Learning of Fuzzy Systems

       • Tomasz Czogala, Jacek M. Leski

       Pages 145-152

   12. The Incremental Method for Discovery of Association Rules

       • Damian Dudek, Aleksander Zgrzywa

       Pages 153-160

   13. Feature Extraction with Wavelet Transformation for Statistical Object Recognition

       • Marcin Grzegorzek, Michael Reinhold, Heinrich Niemann

       Pages 161-168

th This book contains papers accepted for presentation at the 4 International Conference on Computer Recognition Systems CORES'05, May 22-25, 2005, Rydzyna Castle (Poland), This conference is a continuation of a series of con­ ferences on similar topics (KOSYR) organized each second year, since 1999, by the Chair of Systems and Computer Networks, Wroclaw University of Tech­ nology. An increasing interest to those conferences paid not only by home but also by foreign participants inspired the organizers to transform them into conferences of international range. Our expectations that the community of specialists in computer recognizing systems will find CORES'05 a proper form of maintaining the tradition of the former conferences have been confirmed by a large number of submitted papers. Alas, organizational constraints caused a necessity to narrow the acceptance criteria so that only 100 papers have been finally included into the conference program. The area covered by accepted papers is still very large and it shows how vivacious is scientific activity in the domain of computer recognition methods and systems. It contains vari­ ous theoretical approaches to the recognition problem based on mathematical statistics, fuzzy sets, morphological methods, wavelets, syntactic methods, genetic algorithms, artificial neural networks, ontological models, etc. Most attention is still paid to visual objects recognition; however, acoustic, tex­ tual and other objects are also considered. Among application areas medical problems are in majority; recognition of faces, speech signals and textual in­ formation processing methods being also investigated.

• Signal
• architecture
• classification
• cognition
• computer vision
• decision support system
• image processing
• knowledge
• learning
• machine learning
• pattern recognition
• web mining

• Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland

  Marek Kurzyński, Edward Puchała, Michał Woźniak, Andrzej żołnierek

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