By Alexander J. Smola, Peter Bartlett, Bernhard Schölkopf, Dale Schuurmans
The idea that of huge margins is a unifying precept for the research of many various methods to the type of information from examples, together with boosting, mathematical programming, neural networks, and help vector machines. the truth that it's the margin, or self assurance point, of a classification--that is, a scale parameter--rather than a uncooked education blunders that concerns has turn into a key device for facing classifiers. This e-book exhibits how this concept applies to either the theoretical research and the layout of algorithms.The ebook presents an summary of contemporary advancements in huge margin classifiers, examines connections with different equipment (e.g., Bayesian inference), and identifies strengths and weaknesses of the approach, in addition to instructions for destiny study. one of the members are Manfred Opper, Vladimir Vapnik, and beauty Wahba.
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Extra info for Advances in Large-Margin Classifiers (Neural Information Processing)
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