Advances In Large Margin Classifiers; Editor: Smola, Alexander J.; Editor: Scholkopf, Bernhard; Hardback; Book

Editor: Smola, Alexander J.; Editor: Scholkopf, Bernhard
Neural Information Processing; Hardback; Book
422 pages
Published: November 2000
The MIT Press
ISBN: 0262194481

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. AmongAn overview of recent developments. This text provides an overview of developments in large-margin classifiers, examines connections with other methods (e.g. Bayesian inference), identifies strengths and weaknesses of the method and looks at possible directions for future research.