Overview

This book constitutes the refereed proceedings of the International Conference on Artificial Neural Networks,ICANN 2001, held in Vienna, Austria in August 2001. The 171 revised papers presented together with three invited contributions were carefully reviewed and selected from around 300 submissions. The papers are organized in topical sections on data analysis and pattern recognition, theory, kernel methods, topographic mapping, independent component analysis, signal processing, time series processing, agent-based economic modeling, selforganization and dynamical systems, robotics and control, vision and image processing, computational neuroscience, and connectionist and cognitive science.

Full Product Details

Author:   Georg Dorffner ,  Horst Bischof ,  Kurt Hornik
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2001 ed.
Volume:   2130
Weight:   1.701kg
ISBN:  

9783540424864

ISBN 10:   3540424865
Pages:   1262
Publication Date:   13 August 2001
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

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 Keyboard' Controlled by Spontaneous EEG Activity.- Clustering of EEG-Segments Using Hierarchical Agglomerative Methods and Self-Organizing Maps.- Nonlinear Signal Processing for Noise Reduction of Unaveraged Single Channel MEG Data.- Time Series Processing.- A Discrete Probabilistic Memory Model for Discovering Dependencies in Time.- Applying LSTM to Time Series Predictable through Time-Window Approaches.- Generalized Relevance LVQ for Time Series.- Unsupervised Learning in LSTM Recurrent Neural Networks.- Applying Kernel Based Subspace Classification to a Non-intrusive Monitoring for Household Electric Appliances.- Neural Networks in Circuit Simulators.- Neural Networks Ensemble for Cyclosporine Concentration Monitoring.- Efficient Hybrid Neural Network for Chaotic Time Series Prediction.- Online Symbolic-Sequence Prediction with Discrete-Time Recurrent Neural Networks.- Prediction Systems Based on FIR BP Neural Networks.- On the Generalization Ability of Recurrent Networks.- Finite-State Reber Automaton and the Recurrent Neural Networks Trained in Supervised and Unsupervised Manner.- Estimation of Computational Complexity of Sensor Accuracy Improvement Algorithm Based on Neural Networks.- Fusion Architectures for the Classification of Time Series.- Special Session: Agent-Based Economic Modeling.- The Importance of Representing Cognitive Processes in Multi-agent Models.- Multi-agent FX-Market Modeling Based on Cognitive Systems.- Speculative Dynamics in a Heterogeneous-Agent Model.- Nonlinear Adaptive Beliefs and the Dynamics of Financial Markets: The Role of the Evolutionary Fitness Measure.- Analyzing Purchase Data by a Neural Net Extension of the Multinomial Logit Model.- Selforganization and Dynamical Systems.- Using Maximal Recurrence in Linear Threshold Competitive Layer Networks.- Exponential Transients in Continuous-Time Symmetric Hopfield Nets.- Initial Evolution Results on CAM-Brain Machines (CBMs).- Self-Organizing Topology Evolution of Turing Neural Networks.- Efficient Pattern Discrimination with Inhibitory WTA Nets.- Cooperative Information Control to Coordinate Competition and Cooperation.- Qualitative Analysis of Continuous Complex-Valued Associative Memories.- Self Organized Partitioning of Chaotic Attractors for Control.- A Generalisable Measure of Self-Organisation and Emergence.- Market-Based Reinforcement Learning in Partially Observable Worlds.- Sequential Strategy for Learning Multi-stage Multi-agent Collaborative Games.- Robotics and Control.- Neural Architecture for Mental Imaging of Sequences Based on Optical Flow Predictions.- Visual Checking of Grasping Positions of a Three-Fingered Robot Hand.- Anticipation-Based Control Architecture for a Mobile Robot.- Neural Adaptive Force Control for Compliant Robots.- A Design of Neural-Net Based Self-Tuning PID Controllers.- Kinematic Control and Obstacle Avoidance for Redundant Manipulators Using a Recurrent Neural Network.- Adaptive Neural Control of Nonlinear Systems.- A Hierarchical Method for Training Embedded Sigmoidal Neural Networks.- Towards Learning Path Planning for Solving Complex Robot Tasks.- Hammerstein Model Identification Using Radial Basis Functions Neural Networks.- Evolving Neural Behaviour Control for Autonomous Robots.- Construction by Autonomous Agents in a Simulated Environment.- A Neural Control Model Using Predictive Adjustment Mechanism of Viscoelastic Property of the Human Arm.- Multi-joint Arm Trajectory Formation Based on the Minimization Principle Using the Euler-Poisson Equation.- Vision and Image Processing.- Neocognitron of a New Version: Handwritten Digit Recognition.- A Comparison of Classifiers for Real-Time Eye Detection.- Neural Network Analysis of Dynamic Contrast-Enhanced MRI Mammography.- A New Adaptive Color Quantization Technique.- Tunable Oscillatory Network for Visual Image Segmentation.- Detecting Shot Transitions for Video Indexing with FAM.- Finding Faces in Cluttered Still Images with Few Examples.- Description of Dynamic Structured Scenes by a SOM/ARSOM Hierarchy.- Evaluation of Distance Measures for Partial Image Retrieval Using Self-Organizing Map.- Video Sequence Boundary Detection Using Neural Gas Networks.- A Neural-Network-Based Approach to Adaptive Human Computer Interaction.- Adaptable Neural Networks for Unsupervised Video Object Segmentation of Stereoscopic Sequences.- Computational Neuroscience.- A Model of Border-Ownership Coding in Early Vision.- Extracting Slow Subspaces from Natural Videos Leads to Complex Cells.- Neural Coding of Dynamic Stimuli.- Resonance of a Stochastic Spiking Neuron Mimicking the Hodgkin-Huxley Model.- Spike and Burst Synchronization in a Detailed Cortical Network Model with I-F Neurons.- Using Depressing Synapses for Phase Locked Auditory Onset Detection.- Controlling Oscillatory Behaviour of a Two Neuron Recurrent Neural Network Using Inputs.- Temporal Hebbian Learning in Rate-Coded Neural Networks: A Theoretical Approach towards Classical Conditioning.- A Mathematical Analysis of a Correlation Based Model for the Orientation Map Formation.- Learning from Chaos: A Model of Dynamical Perception.- Episodic Memory and Cognitive Map in a Rate Model Network of the Rat Hippocampus.- A Model of Horizontal 360 Degrees Object Localization Based on Binaural Hearing and Monocular Vision.- Self-Organization of Orientation Maps, Lateral Connections, and Dynamic Receptive Fields in the Primary Visual Cortex.- Markov Chain Model Approximating the Hodgkin-Huxley Neuron.- Connectionist Cognitive Science.- A Neural Oscillator Model of Auditory Attention.- Coupled Neural Maps for the Origins of Vowel Systems.- Learning for Text Summarization Using Labeled and Unlabeled Sentences.- On-Line Error Detection of Annotated Corpus Using Modular Neural Networks.- Instance-Based Method to Extract Rules from Neural Networks.- A Novel Binary Spell Checker.- Neural Nets for Short Movements in Natural Language Processing.- Using Document Features to Optimize Web Cache.- Generation of Diversiform Characters Using a Computational Handwriting Model and a Genetic Algorithm.- Information Maximization and Language Acquisition.- A Mirror Neuron System for Syntax Acquisition.- A Network of Relaxation Oscillators that Finds Downbeats in Rhythms.- Knowledge Incorporation and Rule Extraction in Neural Networks.

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