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
Another rigorous textbook on learning theory. Focus on the nonparametric methods. Highly recommended!
评分holy bible
评分從nonparametric statistics的角度研究機器學習算法,主要關注點是算法的consistency(是否能漸進逼近Bayes error),主要使用的工具是幾個中心不等式(尤其是Vapnik-Chervonenkis不等式),分析的算法包括最近鄰、histogram、決策樹等。書中有不少腦洞很大的證明,剛開始看還是挺吃力的。習題都很難,還沒有答案。唯一的缺憾是太老瞭,畢竟是二十年前齣版的。
评分holy bible
评分holy bible
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