Overview
- 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 empirical processes needed to obtain error bounds
- in machine learning and sparse recovery
- Develops a comprehensive theory of excess risk bounds and oracle inequalities for penalized empirical
- risk minimization
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Mathematics (LNM, volume 2033)
Part of the book sub series: École d'Été de Probabilités de Saint-Flour (LNMECOLE)
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Table of contents (9 chapters)
Keywords
About this book
Reviews
From the reviews:
“The book is an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. … The book is interesting and useful for students as well as for professionals in the field of probability theory, statistics, and their applications.” (Pavel Stoynov, Zentralblatt MATH, Vol. 1223, 2011)
Authors and Affiliations
Bibliographic Information
Book Title: Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
Book Subtitle: École d’Été de Probabilités de Saint-Flour XXXVIII-2008
Authors: Vladimir Koltchinskii
Series Title: Lecture Notes in Mathematics
DOI: https://doi.org/10.1007/978-3-642-22147-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2011
Softcover ISBN: 978-3-642-22146-0Published: 29 July 2011
eBook ISBN: 978-3-642-22147-7Published: 29 July 2011
Series ISSN: 0075-8434
Series E-ISSN: 1617-9692
Edition Number: 1
Number of Pages: IX, 254