Advances in Large-Margin Classifiers
Product Description
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.
About the Author
Alexander J. Smola is a Postdoctoral Fellow at GMD-FIRST, Berlin. Peter L. Bartlett is Senior Fellow, Computer Sciences Laboratory, Australian National University. Bernhard Schölkopf is a Researcher at Microsoft Research Ltd., Cambridge, UK. Dale Schuurmans is Assistant Professor of Computer Science at the University of Waterloo, Ontario, Canada.
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