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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models Vojislav Kecman
Publisher: The MIT Press

Implementation issues of neural networks. Subsequently, a theoretical analysis of these techniques is . Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. Support Vector Machines Neural network applications. Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. (165), Masanobu Kittaka and Masafumi Hagiwara: “Language Processing Neural Network with Additional Learning,”International Conference on Soft Computing and Intelligent Systems & ISIS 2008, 2008-09. All the papers in: Environment, Economics, Energy, Devices, Systems, Communications, Computers, Biomedicine and Mathematics accepted, registered and presented in IAASAT conferences will be eligible for publication in several ISI special .. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. Thereafter, different soft computing techniques like neural networks, genetic algorithms, and hybrid approaches are discussed along with their application to gene prediction. (164), Hajime Hotta, Masafumi ( 150), Hajime Hotta, Masafumi Hagiwara:“A Japanese Font Designing System Using Fuzzy-Logic-Based Kansei Database,” International Symposium on Advanced Intelligent Systems (ISIS 2005), pp.723-728, 2005-09. Intelligent Control and Automation (but not limited to): Mathematical modeling and analysis of complex systems. To introduce the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience Adaptive Neuro-Fuzzy Inference Systems – Architecture – Hybrid Learning Algorithm – Learning Methods that Cross-fertilize ANFIS and RBFN – Coactive Neuro Fuzzy Modeling – Framework Neuron Functions for Adaptive Networks – Neuro Fuzzy Spectrum. Learning And Soft Computing | Support Vector Machines, Neural Networks, and Fuzzy Logic Models. The MIT Press: Cambridge , Massachusetts , London , England . In this work three supervised classification methods, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), are used for classification task. Fuzzy Systems, fuzzy logic and possibility theory Computational economics. KECMAN Vojislav (2001), Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models.