Artificial Neural Networks - ICANN 2007 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I

Learning Theory.- Generalization Error of Automatic Relevance Determination.- On a Singular Point to Contribute to a Learning Coefficient and Weighted Resolution of Singularities.- Improving the Prediction Accuracy of Echo State Neural Networks by Anti-Oja’s Learning.- Theoretical Analysis of Accuracy of Gaussian Belief Propagation.- Relevance Metrics to Reduce Input Dimensions in Artificial Neural Networks.- An Improved Greedy Bayesian Network Learning Algorithm on Limited Data.- Incremental One-Class Learning with Bounded Computational Complexity.- Estimating the Size of Neural Networks from the Number of Available Training Data.- A Maximum Weighted Likelihood Approach to Simultaneous Model Selection and Feature Weighting in Gaussian Mixture.- Estimation of Poles of Zeta Function in Learning Theory Using Padé Approximation.- Neural Network Ensemble Training by Sequential Interaction.- Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification.- Advances in Neural Network Learning Methods.- Structure Learning with Nonparametric Decomposable Models.- Recurrent Bayesian Reasoning in Probabilistic Neural Networks.- Resilient Approximation of Kernel Classifiers.- Incremental Learning of Spatio-temporal Patterns with Model Selection.- Accelerating Kernel Perceptron Learning.- Analysis and Comparative Study of Source Separation Performances in Feed-Forward and Feed-Back BSSs Based on Propagation Delays in Convolutive Mixture.- Learning Highly Non-separable Boolean Functions Using Constructive Feedforward Neural Network.- A Fast Semi-linear Backpropagation Learning Algorithm.- Improving the GRLVQ Algorithm by the Cross Entropy Method.- Incremental and Decremental Learning for Linear Support Vector Machines.- An Efficient Method for Pruning the Multilayer Perceptron Based on the Correlation of Errors.- Reinforcement Learning for Cooperative Actions in a Partially Observable Multi-agent System.- Input Selection for Radial Basis Function Networks by Constrained Optimization.- An Online Backpropagation Algorithm with Validation Error-Based Adaptive Learning Rate.- Adaptive Self-scaling Non-monotone BFGS Training Algorithm for Recurrent Neural Networks.- Some Properties of the Gaussian Kernel for One Class Learning.- Improved SOM Learning Using Simulated Annealing.- The Usage of Golden Section in Calculating the Efficient Solution in Artificial Neural Networks Training by Multi-objective Optimization.- Ensemble Learning.- Designing Modular Artificial Neural Network Through Evolution.- Averaged Conservative Boosting: Introducing a New Method to Build Ensembles of Neural Networks.- Selection of Decision Stumps in Bagging Ensembles.- An Ensemble Dependence Measure.- Boosting Unsupervised Competitive Learning Ensembles.- Using Fuzzy, Neural and Fuzzy-Neural Combination Methods in Ensembles with Different Levels of Diversity.- Spiking Neural Networks.- SpikeStream: A Fast and Flexible Simulator of Spiking Neural Networks.- Evolutionary Multi-objective Optimization of Spiking Neural Networks.- Building a Bridge Between Spiking and Artificial Neural Networks.- Clustering of Nonlinearly Separable Data Using Spiking Neural Networks.- Implementing Classical Conditioning with Spiking Neurons.- Advances in Neural Network Architectures.- Deformable Radial Basis Functions.- Selection of Basis Functions Guided by the L2 Soft Margin.- Extended Linear Models with Gaussian Prior on the Parameters and Adaptive Expansion Vectors.- Functional Modelling of Large Scattered Data Sets Using Neural Networks.- Stacking MF Networks to Combine the Outputs Provided by RBF Networks.- Neural Network Processing for Multiset Data.- The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition.- Partially Activated Neural Networks by Controlling Information.- CNN Based Hole Filler Template Design Using Numerical Integration Techniques.- Impact of Shrinking Technologies on the Activation Function of Neurons.- Rectangular Basis Functions Applied to Imbalanced Datasets.- Qualitative Radial Basis Function Networks Based on Distance Discretization for Classification Problems.- A Control Approach to a Biophysical Neuron Model.- Integrate-and-Fire Neural Networks with Monosynaptic-Like Correlated Activity.- Multi-dimensional Recurrent Neural Networks.- FPGA Implementation of an Adaptive Stochastic Neural Model.- Neural Dynamics and Complex Systems.- Global Robust Stability of Competitive Neural Networks with Continuously Distributed Delays and Different Time Scales.- Nonlinear Dynamics Emerging in Large Scale Neural Networks with Ontogenetic and Epigenetic Processes.- Modeling of Dynamics Using Process State Projection on the Self Organizing Map.- Fixed Points of the Abe Formulation of Stochastic Hopfield Networks.- Visualization of Dynamics Using Local Dynamic Modelling with Self Organizing Maps.- Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences.- Data Analysis.- Data Fusion and Auto-fusion for Quantitative Structure-Activity Relationship (QSAR).- Cluster Domains in Binary Minimization Problems.- MaxSet: An Algorithm for Finding a Good Approximation for the Largest Linearly Separable Set.- Generalized Softmax Networks for Non-linear Component Extraction.- Stochastic Weights Reinforcement Learning for Exploratory Data Analysis.- Post Nonlinear Independent Subspace Analysis.- Estimation.- Algebraic Geometric Study of Exchange Monte Carlo Method.- Solving Deep Memory POMDPs with Recurrent Policy Gradients.- Soft Clustering for Nonparametric Probability Density Function Estimation.- Vector Field Approximation by Model Inclusive Learning of Neural Networks.- Spectral Measures for Kernel Matrices Comparison.- A Novel and Efficient Method for Testing Non Linear Separability.- A One-Step Unscented Particle Filter for Nonlinear Dynamical Systems.- Spatial and Spatio-Temporal Learning.- Spike-Timing-Dependent Synaptic Plasticity to Learn Spatiotemporal Patterns in Recurrent Neural Networks.- A Distributed Message Passing Algorithm for Sensor Localization.- An Analytical Model of Divisive Normalization in Disparity-Tuned Complex Cells.- Evolutionary Computing.- Automatic Design of Modular Neural Networks Using Genetic Programming.- Blind Matrix Decomposition Via Genetic Optimization of Sparseness and Nonnegativity Constraints.- Meta Learning, Agents Learning.- Meta Learning Intrusion Detection in Real Time Network.- Active Learning to Support the Generation of Meta-examples.- Co-learning and the Development of Communication.- Complex-Valued Neural Networks (Special Session).- Models of Orthogonal Type Complex-Valued Dynamic Associative Memories and Their Performance Comparison.- Dynamics of Discrete-Time Quaternionic Hopfield Neural Networks.- Neural Learning Algorithms Based on Mappings: The Case of the Unitary Group of Matrices.- Optimal Learning Rates for Clifford Neurons.- Solving Selected Classification Problems in Bioinformatics Using Multilayer Neural Network Based on Multi-Valued Neurons (MLMVN).- Error Reduction in Holographic Movies Using a Hybrid Learning Method in Coherent Neural Networks.- Temporal Synchronization and Nonlinear Dynamics in Neural Networks (Special Session).- Sparse and Transformation-Invariant Hierarchical NMF.- Zero-Lag Long Range Synchronization of Neurons Is Enhanced by Dynamical Relaying.- Polynomial Cellular Neural Networks for Implementing the Game of Life.- Deterministic Nonlinear Spike Train Filtered by Spiking Neuron Model.- The Role of Internal Oscillators for the One-Shot Learning of Complex Temporal Sequences.- Clustering Limit Cycle Oscillators by Spectral Analysis of the Synchronisation Matrix with an Additional Phase Sensitive Rotation.- Control and Synchronization of Chaotic Neurons Under Threshold Activated Coupling.- Neuronal Multistability Induced by Delay.