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Uniform Central Limit Theorems

Author: R. M. Dudley
Publisher: Cambridge University Press
ISBN: 0521461022
Size: 75.65 MB
Format: PDF, ePub, Docs
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This treatise by an acknowledged expert includes several topics not found in any previous book.

Advanced Lectures On Machine Learning

Author: Shahar Mendelson
Publisher: Springer Science & Business Media
ISBN: 3540005293
Size: 56.40 MB
Format: PDF, Mobi
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This book presents revised reviewed versions of lectures given during the Machine Learning Summer School held in Canberra, Australia, in February 2002. The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning. Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.

Concentration Inequalities And Model Selection

Author: Pascal Massart
Publisher: Springer Verlag
ISBN: 9783540484974
Size: 23.18 MB
Format: PDF, Kindle
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Concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn to be essential tools to develop a non asymptotic theory in statistics. This volume provides an overview of a non asymptotic theory for model selection. It also discusses some selected applications to variable selection, change points detection and statistical learning.

Computational Learning Theory

Author: Jyrki Kivinen
Publisher: Springer
ISBN:
Size: 17.62 MB
Format: PDF, Mobi
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This book constitutes the refereed proceedings of the 15th Annual Conference on Computational Learning Theory, COLT 2002, held in Sydney, Australia, in July 2002. The 26 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on statistical learning theory, online learning, inductive inference, PAC learning, boosting, and other learning paradigms.

Introduction To Foliations And Lie Groupoids

Author: I. Moerdijk
Publisher: Cambridge University Press
ISBN: 9781139438988
Size: 70.18 MB
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This book gives a quick introduction to the theory of foliations, Lie groupoids and Lie algebroids. An important feature is the emphasis on the interplay between these concepts: Lie groupoids form an indispensable tool to study the transverse structure of foliations as well as their noncommutative geometry, while the theory of foliations has immediate applications to the Lie theory of groupoids and their infinitesimal algebroids. The book starts with a detailed presentation of the main classical theorems in the theory of foliations then proceeds to Molino's theory, Lie groupoids, constructing the holonomy groupoid of a foliation and finally Lie algebroids. Among other things, the authors discuss to what extent Lie's theory for Lie groups and Lie algebras holds in the more general context of groupoids and algebroids. Based on the authors' extensive teaching experience, this book contains numerous examples and exercises making it ideal for graduate students and their instructors.