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Computer Science - Artificial Intelligence | Artificial Neural Networks - ICANN 2006 - 16th International Conference, Athens, Greece, September

Artificial Neural Networks - ICANN 2006

16th International Conference, Athens, Greece, September 10-14, 2006, Proceedings, Part I

Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (Eds.)

2006, XXXIV, 1008 p. Also available online.

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The two-volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006, held in Athens, Greece, in September 2006. The 208 revised full papers presented were carefully reviewed and selected from 475 submissions.

This is the first volume, which presents 103 papers, organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, learning random neural networks and stochastic agents, hybrid architectures, self organization, connectionist cognitive science, cognitive machines, neural dynamics and complex systems, computational neuroscience, neural control, reinforcement learning and robotics applications, robotics, control, planning, as well as bio-inspired neural network on-chip implementation and applications.

The second volume contains 105 contributions covering such topics as neural networks, semantic web technologies and multimedia analysis, signal and time series processing, data analysis, pattern recognition, vision and image processing, computational finance an

Content Level » Research

Keywords » Neuroscience - algorithm - algorithms - bioinformatics - complex system - image processing - learning - machine learning - multimedia - organization - robot - robotics - semantic web

Related subjects » Artificial Intelligence - Database Management & Information Retrieval - Image Processing - Neuroscience - Theoretical Computer Science

Table of contents 

Feature Selection and Dimension Reduction for Regression (Special Session).- Dimensionality Reduction Based on ICA for Regression Problems.- A Functional Approach to Variable Selection in Spectrometric Problems.- The Bayes-Optimal Feature Extraction Procedure for Pattern Recognition Using Genetic Algorithm.- Speeding Up the Wrapper Feature Subset Selection in Regression by Mutual Information Relevance and Redundancy Analysis.- Effective Input Variable Selection for Function Approximation.- Comparative Investigation on Dimension Reduction and Regression in Three Layer Feed-Forward Neural Network.- Learning Algorithms (I).- On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition.- Learning Long Term Dependencies with Recurrent Neural Networks.- Adaptive On-Line Neural Network Retraining for Real Life Multimodal Emotion Recognition.- Time Window Width Influence on Dynamic BPTT(h) Learning Algorithm Performances: Experimental Study.- Framework for the Interactive Learning of Artificial Neural Networks.- Analytic Equivalence of Bayes a Posteriori Distributions.- Learning Algorithms (II).- Neural Network Architecture Selection: Size Depends on Function Complexity.- Competitive Repetition-suppression (CoRe) Learning.- Real-Time Construction of Neural Networks.- MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation.- A Variational Formulation for the Multilayer Perceptron.- Advances in Neural Network Learning Methods (Special Session).- Natural Conjugate Gradient Training of Multilayer Perceptrons.- Building Ensembles of Neural Networks with Class-Switching.- K-Separability.- Lazy Training of Radial Basis Neural Networks.- Investigation of Topographical Stability of the Concave and Convex Self-Organizing Map Variant.- Alternatives to Parameter Selection for Kernel Methods.- Faster Learning with Overlapping Neural Assemblies.- Improved Storage Capacity of Hebbian Learning Attractor Neural Network with Bump Formations.- Error Entropy Minimization for LSTM Training.- Ensemble Learning.- Can AdaBoost.M1 Learn Incrementally? A Comparison to Learn?+?+? Under Different Combination Rules.- Ensemble Learning with Local Diversity.- A Machine Learning Approach to Define Weights for Linear Combination of Forecasts.- A Game-Theoretic Approach to Weighted Majority Voting for Combining SVM Classifiers.- Improving the Expert Networks of a Modular Multi-Net System for Pattern Recognition.- Learning Random Neural Networks and Stochastic Agents (Special Session).- Evaluating Users’ Satisfaction in Packet Networks Using Random Neural Networks.- Random Neural Networks for the Adaptive Control of Packet Networks.- Hardware Implementation of Random Neural Networks with Reinforcement Learning.- G-Networks and the Modeling of Adversarial Agents.- Hybrid Architectures.- Development of a Neural Net-Based, Personalized Secure Communication Link.- Exact Solutions for Recursive Principal Components Analysis of Sequences and Trees.- Active Learning with the Probabilistic RBF Classifier.- Merging Echo State and Feedforward Neural Networks for Time Series Forecasting.- Language and Cognition Integration Through Modeling Field Theory: Category Formation for Symbol Grounding.- A Methodology for Estimating the Product Life Cycle Cost Using a Hybrid GA and ANN Model.- Self Organization.- Using Self-Organizing Maps to Support Video Navigation.- Self-Organizing Neural Networks for Signal Recognition.- An Unsupervised Learning Rule for Class Discrimination in a Recurrent Neural Network.- On the Variants of the Self-Organizing Map That Are Based on Order Statistics.- On the Basis Updating Rule of Adaptive-Subspace Self-Organizing Map (ASSOM).- Composite Algorithm for Adaptive Mesh Construction Based on Self-Organizing Maps.- A Parameter in the Learning Rule of SOM That Incorporates Activation Frequency.- Nonlinear Projection Using Geodesic Distances and the Neural Gas Network.- Connectionist Cognitive Science.- Contextual Learning in the Neurosolver.- A Computational Model for the Effect of Dopamine on Action Selection During Stroop Test.- A Neural Network Model of Metaphor Understanding with Dynamic Interaction Based on a Statistical Language Analysis.- Strong Systematicity in Sentence Processing by an Echo State Network.- Modeling Working Memory and Decision Making Using Generic Neural Microcircuits.- A Virtual Machine for Neural Computers.- Cognitive Machines (Special Session).- Machine Cognition and the EC Cognitive Systems Projects: Now and in the Future.- A Complex Neural Network Model for Memory Functioning in Psychopathology.- Modelling Working Memory Through Attentional Mechanisms.- A Cognitive Model of Multi-objective Multi-concept Formation.- A Basis for Cognitive Machines.- Neural Model of Dopaminergic Control of Arm Movements in Parkinson’s Disease Bradykinesia.- Occlusion, Attention and Object Representations.- A Forward / Inverse Motor Controller for Cognitive Robotics.- A Computational Model for Multiple Goals.- Neural Dynamics and Complex Systems.- Detection of a Dynamical System Attractor from Spike Train Analysis.- Recurrent Neural Networks Are Universal Approximators.- A Discrete Adaptive Stochastic Neural Model for Constrained Optimization.- Quantum Perceptron Network.- Critical Echo State Networks.- Rapid Correspondence Finding in Networks of Cortical Columns.- Adaptive Thresholds for Layered Neural Networks with Synaptic Noise.- Backbone Structure of Hairy Memory.- Dynamics of Citation Networks.- Computational Neuroscience.- Processing of Information in Synchroneously Firing Chains in Networks of Neurons.- Phase Precession and Recession with STDP and Anti-STDP.- Visual Pathways for Detection of Landmark Points.- A Model of Grid Cells Based on a Path Integration Mechanism.- Temporal Processing in a Spiking Model of the Visual System.- Accelerating Event Based Simulation for Multi-synapse Spiking Neural Networks.- A Neurocomputational Model of an Imitation Deficit Following Brain Lesion.- Temporal Data Encoding and SequenceLearning with Spiking Neural Networks.- Neural Control, Reinforcement Learning and Robotics Applications.- Optimal Tuning of Continual Online Exploration in Reinforcement Learning.- Vague Neural Network Controller and Its Applications.- Parallel Distributed Profit Sharing for PC Cluster.- Feature Extraction for Decision-Theoretic Planning in Partially Observable Environments.- Reinforcement Learning with Echo State Networks.- Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning.- Nearly Optimal Exploration-Exploitation Decision Thresholds.- Dual Adaptive ANN Controllers Based on Wiener Models for Controlling Stable Nonlinear Systems.- Online Stabilization of Chaotic Maps Via Support Vector Machines Based Generalized Predictive Control.- Robotics, Control, Planning.- Morphological Neural Networks and Vision Based Mobile Robot Navigation.- Position Control Based on Static Neural Networks of Anthropomorphic Robotic Fingers.- Learning Multiple Models of Non-linear Dynamics for Control Under Varying Contexts.- A Study on Optimal Configuration for the Mobile Manipulator: Using Weight Value and Mobility.- VSC Perspective for Neurocontroller Tuning.- A Neural Network Module with Pretuning for Search and Reproduction of Input-Output Mapping.- Bio-inspired Neural Network On-Chip Implementation and Applications (Special session).- Physical Mapping of Spiking Neural Networks Models on a Bio-inspired Scalable Architecture.- A Time Multiplexing Architecture for Inter-neuron Communications.- Neuronal Cell Death and Synaptic Pruning Driven by Spike-Timing Dependent Plasticity.- Effects of Analog-VLSI Hardware on the Performance of the LMS Algorithm.- A Portable Electronic Nose (E-Nose) System Based on PDA.- Optimal Synthesis of Boolean Functions by Threshold Functions.- Pareto-optimal Noise and Approximation Properties of RBF Networks.

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