Details

Title: Advances in large margin classifiers
Other creators: Smola Alexander J.; Bartlett Peter; Schölkopf Bernhard; Schuurmans Dale
Organization: IEEE Xplore (Online Service); MIT Press
Imprint: Cambridge, Massachusetts London, England: MIT Press, 2000
Collection: Электронные книги зарубежных издательств; MIT Press eBooks Library; Общая коллекция
Subjects: Алгоритмы; машинное обучение; функции ядра
UDC: 004.85
Language: English
Rights: Доступ по паролю из сети Интернет (чтение, печать)

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The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

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