Machine.Learning.in.Computer.Vision.Springer.Aug.2005.eBook-DDU.pdf

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Machine Learning in Computer
Vision
by
N. SEBE
University of Amsterdam,
The Netherlands
IRA COHEN
HP Research Labs, U.S.A.
ASHUTOSH GARG
Google Inc., U.S.A.
and
THOMAS S. HUANG
University of Illinois at Urbana-Champaign,
Urbana, IL, U.S.A.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN-10 1-4020-3274-9 (HB) Springer Dordrecht, Berlin, Heidelberg, New York
ISBN-10 1-4020-3275-7 (e-book) Springer Dordrecht, Berlin, Heidelberg, New York
ISBN-13 978-1-4020-3274-5 (HB) Springer Dordrecht, Berlin, Heidelberg, New York
ISBN-13 978-1-4020-3275-2 (e-book) Springer Dordrecht, Berlin, Heidelberg, New York
Published by Springer,
P.O. Box 17, 3300 AA Dordrecht, The Netherlands.
Printed on acid-free paper
All Rights Reserved
© 2005 Springer
No part of this work may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, microfilming,
recording or otherwise, without written permission from the Publisher, with the
exception of any material supplied specifically for the purpose of being entered
and executed on a computer system, for exclusive use by the purchaser of the work.
Printed in the Netherlands.
To my parents
Nicu
To Merav and Yonatan
Ira
To my parents
Asutosh
To my students:
Past, present, and future
Tom
Contents
Foreword
Preface
1. INTRODUCTION
1
Research Issues on Learning in Computer Vision
2
Overview of the Book
3
Contributions
2. THEORY:
PROBABILISTIC CLASSIFIERS
1
Introduction
2
Preliminaries and Notations
2.1
Maximum Likelihood Classification
2.2
Information Theory
2.3
Inequalities
3
Bayes Optimal Error and Entropy
4
Analysis of Classification Error of Estimated (
Mismatched
)
Distribution
4.1
Hypothesis Testing Framework
4.2
Classification Framework
5
Density of Distributions
5.1
Distributional Density
5.2
Relating to Classification Error
6
Complex Probabilistic Models and Small Sample Effects
7
Summary
xi
xiii
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41

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