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Smola, Alexander J. (EDT)/ Bartlett, Peter J. (EDT)/ Scholkopf, Bernhard (EDT)/ Schuurmans, Dale (ED 作者

Mit Pr

译者

2000-9 出版日期

422 页数

$ 62.15 价格

HRD

丛书系列

9780262194488 图书编码

Advances in Large-Margin Classifiers 在线电子书图书标签: 机器学习

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Advances in Large-Margin Classifiers 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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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.

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