A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
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Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
评分这本书可以cite到很多folklore,基础理论值得反复重读。minimax部分非常系统。
评分Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
评分这本书可以cite到很多folklore,基础理论值得反复重读。minimax部分非常系统。
评分这本书可以cite到很多folklore,基础理论值得反复重读。minimax部分非常系统。
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