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This book is based on the papers presented at the International Conference on Arti?cial Neural Networks, ICANN 2001, from August 21-25, 2001 at the - enna University of Technology, Austria. The conference is organized by the A- trian Research Institute for Arti?cal Intelligence in cooperation with the Pattern Recognition and Image Processing Group and the Center for Computational - telligence at the Vienna University of Technology. The ICANN conferences were initiated in 1991 and have become the major European meeting in the ?eld of neural networks. From about 300 submitted papers, the program committee selected 171 for publication. Each paper has been reviewed by three program committee m- bers/reviewers. We would like to thank all the members of the program comm- tee and the reviewers for their great e?ort in the reviewing process and helping us to set up a scienti?c program of high quality. In addition, we have invited eight speakers; three of their papers are also included in the proceedings. We would like to thank the European Neural Network Society (ENNS) for their support. We acknowledge the ?nancial support of Austrian Airlines, A- trian Science Foundation (FWF) under the contract SFB 010, Austrian Society for Arti?cial Intelligence (OGAI), Bank Austria, and the Vienna Convention Bureau. We would like to express our sincere thanks to A. Flexer, W. Horn, K. Hraby, F. Leisch, C. Schittenkopf, and A. Weingessel. The conference and the proceedings would not have been possible without their enormous contri- tion.
Inhalt
Invited Papers.- The Complementary Brain (Abstract).- Neural Networks for Adaptive Processing of Structured Data.- Bad Design and Good Performance: Strategies of the Visual System for Enhanced Scene Analysis.- Data Analysis and Pattern Recognition.- Fast Curvature Matrix-Vector Products.- Architecture Selection in NLDA Networks.- Neural Learning Invariant to Network Size Changes.- Boosting Mixture Models for Semi-supervised Learning.- Bagging Can Stabilize without Reducing Variance.- Symbolic Prosody Modeling by Causal Retro-causal NNs with Variable Context Length.- Discriminative Dimensionality Reduction Based on Generalized LVQ.- A Computational Intelligence Approach to Optimization with Unknown Objective Functions.- Clustering Gene Expression Data by Mutual Information with Gene Function.- Learning to Learn Using Gradient Descent.- A Variational Approach to Robust Regression.- Minimum-Entropy Data Clustering Using Reversible Jump Markov Chain Monte Carlo.- Behavioral Market Segmentation of Binary Guest Survey Data with Bagged Clustering.- Direct Estimation of Polynomial Densities in Generalized RBF Networks Using Moments.- Generalisation Improvement of Radial Basis Function Networks Based on Qualitative Input Conditioning for Financial Credit Risk Prediction.- Approximation of Bayesian Discriminant Function by Neural Networks in Terms of Kullback-Leibler Information.- The Bias-Variance Dilemma of the Monte Carlo Method.- A Markov Chain Monte Carlo Algorithm for the Quadratic Assignment Problem Based on Replicator Equations.- Mapping Correlation Matrix Memory Applications onto a Beowulf Cluster.- Accelerating RBF Network Simulation by Using Multimedia Extensions of Modern Microprocessors.- A Game-Theoretic Adaptive Categorization Mechanism for ART-Type Networks.- Gaussian Radial Basis Functions and Inner-Product Spaces.- Mixture of Probabilistic Factor Analysis Model and Its Applications.- Deferring the Learning for Better Generalization in Radial Basis Neural Networks.- Improvement of Cluster Detection and Labeling Neural Network by Introducing Elliptical Basis Function.- Independent Variable Group Analysis.- Weight Quantization for Multi-layer Perceptrons Using Soft Weight Sharing.- Voting-Merging: An Ensemble Method for Clustering.- The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks.- Transductive Learning: Learning Iris Data with Two Labeled Data.- Approximation of Time-Varying Functions with Local Regression Models.- Theory.- Complexity of Learning for Networks of Spiking Neurons with Nonlinear Synaptic Interactions.- Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension.- Generalization Performances of Perceptrons.- Bounds on the Generalization Ability of Bayesian Inference and Gibbs Algorithms.- Learning Curves for Gaussian Processes Models: Fluctuations and Universality.- Tight Bounds on Rates of Neural-Network Approximation.- Kernel Methods.- Scalable Kernel Systems.- On-Line Learning Methods for Gaussian Processes.- Online Approximations for Wind-Field Models.- Fast Training of Support Vector Machines by Extracting Boundary Data.- Multiclass Classification with Pairwise Coupled Neural Networks or Support Vector Machines.- Incremental Support Vector Machine Learning: A Local Approach.- Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers.- Sparse Kernel Regressors.- Learning on Graphs in the Game of Go.- Nonlinear Feature Extraction Using Generalized Canonical Correlation Analysis.- Gaussian Process Approach to Stochastic Spiking Neurons with Reset.- Kernel Based Image Classification.- Gaussian Processes for Model Fusion.- Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines.- Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines.- Close-Class-Set Discrimination Method for Recognition of Stop_Consonant-Vowel Utterances Using Support Vector Machines.- Linear Dependency between ? and the Input Noise in ?-Support Vector Regression.- The Bayesian Committee Support Vector Machine.- Topographic Mapping.- Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds.- Self Organizing Map and Sammon Mapping for Asymmetric Proximities.- Active Learning with Adaptive Grids.- Complex Process Visualization through Continuous Feature Maps Using Radial Basis Functions.- A Soft k-Segments Algorithm for Principal Curves.- Product Positioning Using Principles from the Self-Organizing Map.- Combining the Self-Organizing Map and K-Means Clustering for On-Line Classification of Sensor Data.- Histogram Based Color Reduction through Self-Organized Neural Networks.- Sequential Learning for SOM Associative Memory with Map Reconstruction.- Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study.- A Topological Hierarchical Clustering: Application to Ocean Color Classification.- Hierarchical Clustering of Document Archives with the Growing Hierarchical Self-Organizing Map.- Independent Component Analysis.- Blind Source Separation of Single Components from Linear Mixtures.- Blind Source Separation Using Principal Component Neural Networks.- Blind Separation of Sources by Differentiating the Output Cumulants and Using Newton's Method.- Mixtures of Independent Component Analysers.- Conditionally Independent Component Extraction for Naive Bayes Inference.- Fast Score Function Estimation with Application in ICA.- Health Monitoring with Learning Methods.- Breast Tissue Classification in Mammograms Using ICA Mixture Models.- Neural Network Based Blind Source Separation of Non-linear Mixtures.- Feature Extraction Using ICA.- Signal Processing.- Continuous Speech Recognition with a Robust Connectionist/Markovian Hybrid Model.- Faster Convergence and Improved Performance in Least-Squares Training of Neural Networks for Active Sound Cancellation.- Bayesian Independent Component Analysis as Applied to One-Channel Speech Enhancement.- Massively Parallel Classification of EEG Signals Using Min-Max Modular Neural Networks.- Single Trial Estimation of Evoked Potentials Using Gaussian Mixture Models with Integrated Noise Component.- A Probabilistic Approach to High-Resolution Sleep Analysis.- Comparison of Wavelet Thresholding Methods for Denoising ECG Signals.- Evoked Potential Signal Estimation Using Gaussian Radial Basis Function Network.- 'Virtual Ke…