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| The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science resarch forum available. The scope of LNCS, including its subseries LNAI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. The type of material publised traditionally includes. -proceedings(published in time for the respective conference) -post-proceedings(consisting of thoroughly revised final full papers) -research monographs(which may be basde on outstanding PhD work, research projects, technical reports, etc.) |
| 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 …… Ensemble Learning Learning Random Neural Networks ansd Stochastic Agents (Special Session) Hybrid Architectures Self Organizxation Connectionist Cognitive Science Cogntive Machines(Special Session) Neural Dynamics and Complex Systems Computational Neurscience Neural Control,Reinforcement Learning and Robotics Applications Robotics,Control,Planning Bio-ingspired Neural Network On-Chip Implementation and Applications (Special session) Author Index |
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