Contents -- Series Editor's Preface -- Volume Editor's Preface -- Chapter 1 Dimensionality Reduction Techniques for Interactive Visualization, Exploratory Data Analysis and Classification -- 1.1 Introduction -- 1.2 Feature Extraction and Multivariate Data Projection -- 1.3 Interactive Data Visualisation and Explorative Analysis -- 1.4 Advanced Projection Methods -- 1.5 Conclusions and Future Work -- References -- Chapter 2 The Self-Organizing Map as a Tool in Knowledge Engineering -- 2.1 Introduction -- 2.2 Data analysis using the Self-Organizing Map -- 2.3 Visualization -- 2.4 Software -- 2.5 Case studies -- 2.6 Conclusions -- 2.7 Acknowledgments -- References -- Chapter 3 Classification of Oceanic Water Types Using Self-organizing Feature Maps -- 3.1 Introduction -- 3.2 Unsupervised neural networks for ocean colour data processing -- 3.3 Hierarchy of neural networks for the water type classification -- 3.4 Accomplishments of the hierarchical image processing -- 3.5 Conclusions -- References -- Chapter 4 Feature Selection by Artificial Neural Network for Pattern Classification -- 4.1 Introduction -- 4.2 Fractal Neural Network Model -- 4.3 Feature Selection Algorithm -- 4.4 Simulation and Results -- 4.5 Discussion and Conclusion -- References -- Chapter 5 MLP Based Character Recognition using Fuzzy Features and a Genetic Algorithm for Feature Selection -- 5.1 Introduction -- 5.2 Hough Transform and Multilayer Perceptron -- 5.3 Fuzzy Feature Extraction using Hough Transform -- 5.4 Multilayer Perceptron with Fuzzy Input and Output -- 5.5 Feature Selection using Genetic Algorithm -- 5.6 Noise Model and Simulation Results -- 5.7 Implementation Results and Discussions -- References.

Chapter 6 A New Clustering with Estimation of Cluster Number Based on Genetic Algorithms -- 6.1 Introduction -- 6.2 Preliminaries -- 6.3 Fuzzy clustering based on the normal distribution -- 6.4 Genetic algorithms -- 6.5 Validity measure for clustering result -- 6.6 Experimental result -- 6.7 Conclusions -- References -- Chapter 7 Associative Classification Method -- 7.1 Introduction -- 7.2 Associatron and Associative memory -- 7.3 Classification procedure based on associative memory -- 7.4 Example of associative classification using the Associatron -- 7.5 Conclusion -- 7.6 Acknowledgment -- Bibliography -- Chapter 8 Recognition of Shapes and Shape Changes in 3D-Objects by GRBF Network: A Structural Learning Algorithm to Explore Small-Sized Networks -- 8.1 Introduction -- 8.2 GRBF network for rigid objects -- 8.3 GRBF network for flexible objects -- 8.4 Structural learning of GRBF network -- 8.5 Recognition of hand shape change -- 8.6 Hand's motion capture system -- 8.7 Conclusions -- References -- Chapter 9 Non-Linear Discriminant Analysis Using Feed-Forward Neural Networks -- 9.1 Introduction -- 9.2 Neural Discriminant Analysis -- 9.3 Application -- 9.4 Conclusion -- References -- Chapter 10 Minimizing the Measurement Cost in the Classification of New Samples by Neural-Network-Based Classifiers -- 10.1 Introduction -- 10.2 Problem Formulation -- 10.3 Classification of New Patterns -- 10.4 Determination of Measurement Order -- 10.5 Computer Simulations -- 10.6 Subdivision Methods -- 10.7 Conclusions -- References -- Chapter 11 Extraction of Fuzzy Rules from Numerical Data for Classifiers -- 11.1 Introduction -- 11.2 Pao-Hu Method (PHM) -- 11.3 Proposed Method -- 11.4 Choice of Membership Functions -- 11.5 Illustration of DP Table and Rule Generation.

11.6 Some Issues Relating to the Rule Generation Scheme -- 11.7 Classification of Test Data Using the Generated Rules -- 11.8 Tuning With Genetic Algorithms -- 11.9 Results -- 11.10 Conclusion and Discussion -- References -- Chapter 12 Genetic Programming based Texture Filtering Framework -- 12.1 Introduction -- 12.2 The LUCIFER2 Framework -- 12.3 Results -- References -- Chapter 13 A Texture Image Segmentation Method Using Neural Networks and Binary Features -- 13.1 Introduction -- 13.2 Texture Feature Extraction -- 13.3 Band-Pass Filter Neural Networks -- 13.4 Pyramid Linking Method for Image Segmentation -- 13.5 Image Segmentation by Using Pyramid Linking and Neural Networks -- 13.6 Experiments -- 13.7 Conclusion and future works -- References -- Chapter 14 Image Retrieval System Based on Subjective Information -- 14.1 Introduction -- 14.2 Overview of the Visual Perception Model -- 14.3 Requirements for Multimedia Database -- 14.4 Experimental Framework -- 14.5 Experimental Results -- 14.6 Concluding Remarks and Future Perspectives -- References -- About the Authors -- Keyword Index.