{"product_id":"least-squares-support-vector-machines-9789812381514","title":"Least Squares Support Vector Machines","description":"\u003cp\u003e • Author(s): Johan A. K. Suykens | Tony Van Gestel | Joseph De Brabanter\u003cbr\u003e • Publisher: World Scientific Publishing Company\u003cbr\u003e • Publisher Imprint: World Scientific Publishing Company\u003cbr\u003e • BISAC: Data Science - Neural Networks\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e","brand":"World Scientific Publishing Company","offers":[{"title":"Hardcover","offer_id":46897618419863,"sku":"9789812381514","price":11408.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9789812381514.webp?v=1770363639","url":"https:\/\/atlanticbooks.com\/products\/least-squares-support-vector-machines-9789812381514","provider":"Atlantic Books","version":"1.0","type":"link"}