文档介绍：Machine Learning puter
Vision
by
N. SEBE
University of Amsterdam,
herlands
IRA COHEN
HP Research Labs, .
ASHUTOSH GARG
Google Inc., .
and
THOMAS S. HUANG
University of Illinois at Urbana-Champaign,
Urbana, IL, .
A . 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,
. Box 17, 3300 AA Dordrecht, herlands.
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 puter system, for exclusive use by the purchaser of the work.
Printed in herlands.
To my parents
Nicu
To Merav and Yonatan
Ira
Tomy parents
Asutosh
To my students:
Past, present, andfuture
Tom
Contents
Foreword xi
Preface xiii
1. INTRODUCTION 1
1 Research Issues on Learning puter Vision 2
2 Overview of the Book 6
3Contributions 12
2. THEORY:
PROBABILISTIC CLASSIFIERS 15
1 Introduction 15
2 Preliminaries and Notations 18
Maximum LikelihoodClassification 18
Information Theory 19
Inequalities 20
3 Bayes Optimal Error and Entropy 20
4 Analysis of Classification Error of Estimated (Mismatched)
Distribution 27
Hypothesis Testing Framework 28
Classification Framework 30
5 Density of Distributions 31
Distributional Density 33
Relating to Classification Error 37
plex Probabilistic Mode