The Resource Support vector machines, Ingo Steinwart, Andreas Christmann, (electronic resource)

Support vector machines, Ingo Steinwart, Andreas Christmann, (electronic resource)

Label
Support vector machines
Title
Support vector machines
Statement of responsibility
Ingo Steinwart, Andreas Christmann
Creator
Contributor
Provider
Subject
Genre
Language
eng
Summary
This 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
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Steinwart, Ingo
Image bit depth
0
LC call number
Q325.5
LC item number
.S74 2008eb
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • SpringerLink
  • Christmann, Andreas
Series statement
Information Science and Statistics,
http://library.link/vocab/subjectName
  • Machine learning
  • Computer Science
  • Artificial Intelligence (incl. Robotics)
  • Data Mining and Knowledge Discovery
  • Pattern Recognition
  • Probability and Statistics in Computer Science
  • Signal, Image and Speech Processing
  • Artificial intelligence
  • Data mining
  • Optical pattern recognition
  • COMPUTERS
  • COMPUTERS
  • Informatique
  • Machine learning
  • Machine learning
  • Electronic books
Label
Support vector machines, Ingo Steinwart, Andreas Christmann, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Loss 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
Dimensions
unknown
Edition
1st ed.
Extent
1 online resource (xvi, 601 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9780387772424
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations (some color).
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-0-387-77241-7
System control number
  • (OCoLC)288440412
  • (OCoLC)ocn288440412
Label
Support vector machines, Ingo Steinwart, Andreas Christmann, (electronic resource)
Publication
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Loss 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
Dimensions
unknown
Edition
1st ed.
Extent
1 online resource (xvi, 601 pages)
File format
multiple file formats
Form of item
  • online
  • electronic
Isbn
9780387772424
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations (some color).
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
Stock number
978-0-387-77241-7
System control number
  • (OCoLC)288440412
  • (OCoLC)ocn288440412

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