This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007.
The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks.
Klappentext
This book is the first of a two-volume set that constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, September 2007. Coverage includes advances in neural network learning methods, advances in neural network architectures, neural dynamics and complex systems, data analysis, evolutionary computing, agents learning, as well as temporal synchronization and nonlinear dynamics in neural networks.
Inhalt
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.-…