Support Vector Machines下载

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Support Vector Machines
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This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 5, MAY 2007 Jayadeva, Senior Member, IEEE, R. Khemchandani, Student Member, IEEE, and Suresh Chandra Index Terms—Support vector machines, pattern classification, machine learning, generalized eigenvalues, eigenvalues, eigenvectors.

An Introduction to Support Vector Machines and Other Kernel-based Learning Method

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini and John Shawe-Taylor ISBN:0521780195 Cambridge University Press ?2000 (190 pages) This is the first comprehensive introduction to SVMs, a new generation learning system based on recent advances in statistical learning theory; it will help readers understand the theory and its real-world applications.

This note is about the Support Vector Machines (SVM), a method that has exhibited excellent accuracy in classication problems. Complex mathematics are involved in various aspects of SVM, e.g., proof of its generalization bounds, its optimization, designing and proving the validity of various non-linear kernels, etc. We will, however, not focus on the mathematics. The main purpose of this note is to introduce how the ideas in SVM are formulated, why are they reasonable, and how various simplications are useful in shaping the SVM primal form. We will not touch any generalization bound of SVM, although that is an area which have attracted intensive research eorts. We will not talk about how the optimization problem in SVM can be solved or approximately solved (for eciency and scalability). These choices enable us to focus on the key ideas that lead to SVM, and the strategies in SVM that may be helpful in other domains, and we encourage the readers to also pay attention to these aspects of SVM.

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