By Alexander J. Smola, Peter Bartlett, Bernhard Schölkopf, Dale Schuurmans
The idea that of enormous 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 aid 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 issues has turn into a key instrument for facing classifiers. This ebook indicates how this concept applies to either the theoretical research and the layout of algorithms.The e-book offers 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 process, in addition to instructions for destiny study. one of the members are Manfred Opper, Vladimir Vapnik, and charm Wahba.
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Extra resources for Advances in Large-Margin Classifiers (Neural Information Processing)
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