| 书目名称 | Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems | | 副标题 | École d’Été de Proba | | 编辑 | Vladimir Koltchinskii | | 视频video | | | 概述 | Provides a unified framework for machine learning problems (such as large margin.classification), sparse recovery and low rank matrix problems.Develops a variety of probabilistic inequalities for empi | | 丛书名称 | Lecture Notes in Mathematics | | 图书封面 |  | | 描述 | The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful. | | 出版日期 | Book 2011 | | 关键词 | 62J99, 62H12, 60B20, 60G99; concentration inequalities; empirical processes; low rank matrix recovery; s | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-22147-7 | | isbn_softcover | 978-3-642-22146-0 | | isbn_ebook | 978-3-642-22147-7Series ISSN 0075-8434 Series E-ISSN 1617-9692 | | issn_series | 0075-8434 | | copyright | Springer-Verlag Berlin Heidelberg 2011 |
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发表于 2025-3-21 16:49:13
