04931nam 2200661Ia 450 991077750140332120230607221456.0981-277-665-6(CKB)1000000000412332(EBL)1681619(OCoLC)879025543(SSID)ssj0000190761(PQKBManifestationID)11215653(PQKBTitleCode)TC0000190761(PQKBWorkID)10180237(PQKB)11166554(MiAaPQ)EBC1681619(WSP)00005089(Au-PeEL)EBL1681619(CaPaEBR)ebr10201197(CaONFJC)MIL505384(EXLCZ)99100000000041233220020813d2002 uy 0engur|n|---|||||txtccrLeast squares support vector machines[electronic resource] /Johan A.K. Suykens ... [et al.]River Edge, NJ World Scientific20021 online resource (308 p.)Description based upon print version of record.981-238-151-1 Includes bibliographical references and index.Contents ; Preface ; Chapter 1 Introduction ; 1.1 Multilayer perceptron neural networks ; 1.2 Regression and classification ; 1.3 Learning and generalization ; 1.3.1 Weight decay and effective number of parameters ; 1.3.2 Ridge regression ; 1.3.3 Bayesian learning1.4 Principles of pattern recognition 1.4.1 Bayes rule and optimal classifier under Gaussian assumptions ; 1.4.2 Receiver operating characteristic ; 1.5 Dimensionality reduction methods ; 1.6 Parametric versus non-parametric approaches and RBF networks1.7 Feedforward versus recurrent network models Chapter 2 Support Vector Machines ; 2.1 Maximal margin classification and linear SVMs ; 2.1.1 Margin ; 2.1.2 Linear SVM classifier: separable case ; 2.1.3 Linear SVM classifier: non-separable case ; 2.2 Kernel trick and Mercer condition2.3 Nonlinear SVM classifiers 2.4 VC theory and structural risk minimization ; 2.4.1 Empirical risk versus generalization error ; 2.4.2 Structural risk minimization ; 2.5 SVMs for function estimation ; 2.5.1 SVM for linear function estimation2.5.2 SVM for nonlinear function estimation 2.5.3 VC bound on generalization error ; 2.6 Modifications and extensions ; 2.6.1 Kernels ; 2.6.2 Extension to other convex cost functions ; 2.6.3 Algorithms ; 2.6.4 Parametric versus non-parametric approachesChapter 3 Basic Methods of Least Squares Support Vector Machines 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 PMachine learningAlgorithmsKernel functionsLeast squaresMachine learning.Algorithms.Kernel functions.Least squares.006.3/1Suykens Johan A. K22315MiAaPQMiAaPQMiAaPQBOOK9910777501403321Least squares support vector machines711830UNINA