{"product_id":"9781489989635","title":"Support Vector Machines","description":"\u003cp\u003eThis book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and their computational efficiency compared to several other methods. \u003c\/p\u003e\u003cp\u003eSince their appearance in the early nineties, support vector machines and related kernel-based methods have been successfully applied in diverse fields of application such as bioinformatics, fraud detection, construction of insurance tariffs, direct marketing, and data and text mining. As a consequence, SVMs now play an important role in statistical machine learning and are used not only by statisticians, mathematicians, and computer scientists, but also by engineers and data analysts.\u003c\/p\u003e\u003cp\u003eThe book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field.\u003c\/p\u003e\u003cp\u003eThe book covers all important topics concerning support vector machines such as: loss functions and their role in the learning process; reproducing kernel Hilbert spaces and their properties; a thorough statistical analysis that uses both traditional uniform bounds and more advanced localized techniques based on Rademacher averages and Talagrand's inequality; a detailed treatment of classification and regression; a detailed robustness analysis; and a description of some of the most recent implementation techniques. To make the book self-contained, an extensive appendix is added which provides the reader with the necessary background from statistics, probability theory, functional analysis, convex analysis, and topology.\u003c\/p\u003e\u003cp\u003eIngo Steinwart is a researcher in the machine learning group at the Los Alamos National Laboratory. He works on support vector machines and related methods.\u003c\/p\u003e\u003cp\u003eAndreas Christmann is Professor of Shastics in the Department of Mathematics at the University of Bayreuth. He works in particular on support vector machines and robust statistics.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer New York","offers":[{"title":"Default Title","offer_id":47031743971568,"sku":"9781489989635","price":189.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781489989635_p0.jpg?v=1763655352","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781489989635","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}