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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models by Vojislav Kecman

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models Vojislav Kecman ebook
Page: 576
Publisher: The MIT Press
Format: pdf
ISBN: 0262112558, 9780262112550

The MIT Press: Cambridge , Massachusetts , London , England . Fuzzy Systems, fuzzy logic and possibility theory Computational economics. Lisp - A Practical Theory of Programming - Eric C.R. Implementation issues of neural networks. Learning And Soft Computing - Support Vector Machines, Neural Networks, And Fuzzy Logic Models - Vojislav Kecman.pdf. Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. Support Vector Machines Neural network applications. Learning And Soft Computing | Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Intelligent Control and Automation (but not limited to): Mathematical modeling and analysis of complex systems. The model produced by support vector classification (as described above) only depends on a subset of the training data, because the cost function for building the model does not care about training points that lie beyond the margin. Thorough introduction to the field of learning from experimental data and soft computing. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. 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.