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首页Introduction to Machine Learning (Second Edition)
1 Introduction 1 2 Supervised Learning 21 3 Bayesian Decision Theory 47 4 Parametric Methods 61 5 Multivariate Methods 87 6 Dimensionality Reduction 109 7 Clustering 143 8 Nonparametric Methods 163 9 Decision Trees 185 10 Linear Discrimination 209 11 Multilayer Perceptrons 233 12 Local Models 279 13 Kernel Machines 309 14 Bayesian Estimation 341 15 Hidden Markov Models 363 16 Graphical Models 387 17 Combining Multiple Learners 419 18 Reinforcement Learning 447 19 Design and Analysis of Machine Learning Experiments 475 A Probability
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Introduction
to
Machine
Learning
Second
Edition
Adaptive Computation and Machine Learning
Thomas Dietterich, Editor
Christopher Bishop, David Heckerman, Michael Jordan, and Michael
Kearns, Associate Editors
A complete list of books published in The Adaptive Computation and
Machine Learning series appears at the back of this book.
Introduction
to
Machine
Learning
Second
Edition
Ethem Alpaydın
The MIT Press
Cambridge, Massachusetts
London, England
© 2010 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any
electronic or mechanical means (including photocopying, recording, or informa-
tion storage and retrieval) without permission in writing from the publisher.
For information about special quantity discounts, please email
special_sales@mitpress.mit.edu.
Typeset in 10/13 Lucida Bright by the author using L
A
T
E
X2
ε
.
Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Information
Alpaydin, Ethem.
Introduction to machine learning / Ethem Alpaydin. — 2nd ed.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-262-01243-0 (hardcover : alk. paper)
1. Machine learning. I. Title
Q325.5.A46 2010
006.3’1—dc22 2009013169
CIP
10987654321
Brief Contents
1Introduction 1
2 Supervised Learning 21
3 Bayesian Decision Theory 47
4 Parametric Methods 61
5 Multivariate Methods 87
6 Dimensionality Reduction 109
7 Clustering 143
8 Nonparametric Methods 163
9 Decision Trees 185
10 Linear Discrimination 209
11 Multilayer Perceptrons 233
12 Local Models 279
13 Kernel Machines 309
14 Bayesian Estimation 341
15 Hidden Markov Models 363
16 Graphical Models 387
17 Combining Multiple Learners 419
18 Reinforcement Learning 447
19 Design and Analysis of Machine Learning Experiments 475
A Probability 517
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