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.
評分
評分
評分
評分
holy bible
评分Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
评分Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
评分Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
评分Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
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