Titlebook: Support Vector Machines for Pattern Classification; Shigeo Abe Book 2010Latest edition Springer-Verlag London 2010 Fuzzy Systems.Kernel Me

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2191-6586 cation rather than covering the theoretical aspects of Suppo.A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as

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Variants of Support Vector Machines,training, learning using privileged information, semi-supervised learning, multiple classifier systems, multiple kernel learning, and other topics: confidence level and visualization of support vector machines.

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Variants of Support Vector Machines,ort vector machines, linear programming support vector machines, sparse support vector machines, etc. We also discuss learning paradigms: incremental training, learning using privileged information, semi-supervised learning, multiple classifier systems, multiple kernel learning, and other topics: co

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Training Methods,raining data. Computational complexity is of the order of .., where . is the number of training data. Thus when . is large, training takes long time. To speed up training, numerous methods have been proposed. One is to extract support vector candidates from the training data and then train the suppo

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Kernel-Based Methods,ques have been extended to incorporate maximizing margins and mapping to a feature space. For example, perceptron algorithms [1–4], neural networks (Chapter 9), and fuzzy systems (Chapter 10) have incorporated maximizing margins and/or mapping to a feature space.

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2191-6586 training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors..978-1-4471-2548-8978-1-84996-098-4Series ISSN 2191-6586 Series E-ISSN 2191-6594

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Book 2010Latest edition learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors..