| 書目名稱 | Empirical Inference | | 副標(biāo)題 | Festschrift in Honor | | 編輯 | Bernhard Sch?lkopf,Zhiyuan Luo,Vladimir Vovk | | 視頻video | http://file.papertrans.cn/309/308861/308861.mp4 | | 概述 | Honours one of the pioneers of machine learning.Contributing authors are among the leading authorities in these domains.Of interest to researchers and engineers in the fields of machine learning, stat | | 圖書封面 |  | | 描述 | .This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning..?.Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik‘s contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the e | | 出版日期 | Book 2013 | | 關(guān)鍵詞 | Bayesian theory; Kernels; Learning; Machine learning; Optimization; Statistical learning theory; Support v | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-41136-6 | | isbn_softcover | 978-3-662-52511-1 | | isbn_ebook | 978-3-642-41136-6 | | copyright | Springer-Verlag Berlin Heidelberg 2013 |
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