000			04722nam a22005535i 4500
001			978-0-387-77242-4
003			DE-He213
005			20161121230713.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387772424 _9978-0-387-77242-4
024	7		_a10.1007/978-0-387-77242-4 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aChristmann, Andreas. _eauthor.
245	1	0	_aSupport Vector Machines _h[electronic resource] / _cby Andreas Christmann, Ingo Steinwart.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_aXVI, 601 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aInformation Science and Statistics, _x1613-9011
505	0		_aLoss Functions and Their Risks -- Surrogate Loss Functions (*) -- Kernels and Reproducing Kernel Hilbert Spaces -- Infinite-Sample Versions of Support VectorMachines -- Basic Statistical Analysis of SVMs -- Advanced Statistical Analysis of SVMs (*) -- Support Vector Machines for Classification -- Support Vector Machines for Regression. -- Robustness -- Computational Aspects -- Data Mining.
520			_aThis 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. Since 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. The 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. The 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. Ingo Steinwart is a researcher in the machine learning group at the Los Alamos National Laboratory. He works on support vector machines and related methods. Andreas 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.
650		0	_aComputer science.
650		0	_aMathematical statistics.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aPattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aPattern Recognition.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aSignal, Image and Speech Processing.
700	1		_aSteinwart, Ingo. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387772417
830		0	_aInformation Science and Statistics, _x1613-9011
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-77242-4
912			_aZDB-2-SCS
950			_aComputer Science (Springer-11645)
999			_c502718 _d502718