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.

Advances in Large-Margin Classifiers (Neural Information Processing) PDF

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