Advances in large margin classifiers - Smola, Alexander J. (Cambridge, Mass. : MIT Press, 2000)
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.
Includes bibliographical references (pages 389-407) and index.
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English.
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Keywords: Machine learning; Algorithms; Kernel functions; Computer algorithms; Algorithms; Machine Learning; Apprentissage automatique; Algorithmes; Noyaux (Mathématiques); algorithms; COMPUTERS -- Enterprise Applications -- Business Intelligence Tools; COMPUTERS -- Intelligence (AI) & Semantics; Computer algorithms; Algorithms; Kernel functions; Machine learning; COMPUTER SCIENCE/General