{"product_id":"least-squares-support-vector-machines","title":"Least Squares Support Vector Machines","description":"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.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":54261940126040,"sku":"9789812381514","price":112.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9789812381514_4aa6179c-2186-4429-a457-d47cc7a2f768.jpg?v=1777286714","url":"https:\/\/agendabookshop.com\/products\/least-squares-support-vector-machines","provider":"Agenda Bookshop","version":"1.0","type":"link"}