{"product_id":"support-vector-machines-9780387772417","title":"Support Vector Machines","description":"\u003cp\u003e • Author(s): Ingo Steinwart\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Computer Science\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\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 Stochastics in the Department of Mathematics at the University of Bayreuth. He works in particular on support vector machines and robust statistics.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Hardcover","offer_id":45275816919191,"sku":"9780387772417","price":16893.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780387772417.webp?v=1769283982","url":"https:\/\/atlanticbooks.com\/products\/support-vector-machines-9780387772417","provider":"Atlantic Books","version":"1.0","type":"link"}