Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings
Wlodzislaw Duch, Janusz Kacprzyk, Erkki Oja, Slawomir Zadrozny
Springer Science & Business Media, Aug 31, 2005 - Computers - 1045 pages
The two volume set LNCS 3696 and LNCS 3697 constitutes the refereed proceedings of the 15th International Conference on Artificial Neural Networks, ICANN 2005, held in Warsaw, Poland in September 2005. The over 600 papers submitted to ICANN 2005 were thoroughly reviewed and carefully selected for presentation. The first volume includes 106 contributions related to Biological Inspirations; topics addressed are modeling the brain and cognitive functions, development of cognitive powers in embodied systems spiking neural networks, associative memory models, models of biological functions, projects in the area of neuroIT, evolutionary and other biological inspirations, self-organizing maps and their applications, computer vision, face recognition and detection, sound and speech recognition, bioinformatics, biomedical applications, and information- theoretic concepts in biomedical data analysis. The second volume contains 162 contributions related to Formal Models and their Applications and deals with new neural network models, supervised learning algorithms, ensemble-based learning, unsupervised learning, recurent neural networks, reinforcement learning, bayesian approaches to learning, learning theory, artificial neural networks for system modeling, decision making, optimalization and control, knowledge extraction from neural networks, temporal data analysis, prediction and forecasting, support vector machines and kernel-based methods, soft computing methods for data representation, analysis and processing, data fusion for industrial, medical and environmental applications, non-linear predictive models for speech processing, intelligent multimedia and semantics, applications to natural language processing, various applications, computational intelligence in games, and issues in hardware implementation.
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activation analysis applied approach approximation architecture Artificial Neural Networks average backpropagation Bayesian network Berlin Heidelberg 2005 calculated classifier clustering combination Computer cross-validation data fusion data set database defined density detection dimensional distribution domain Duch dynamics ensemble equation error estimation example experiments extraction function fusion fuzzy Gaussian genetic algorithm hidden layer ICANN IEEE initial iteration kernel learning algorithm linear LNCS Machine Learning mapping matrix measure method multilayer perceptron neurons nodes nonlinear obtained optimal output overfitting paper parameters patterns perceptron performance prediction presented probabilistic probability problem Proc proposed random recognition recurrent recurrent neural networks regression represents robot rule samples selection semantic sensors signal simulation solution space Springer-Verlag Berlin Heidelberg structure Support Vector Machines Table target task techniques tion training data training set update values variables Volterra series weights