Introduction_to_Machine_Learning__2004_(1).pdf

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Introduction to Machine Learning
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Introduction
to
Machine
Learning
Ethem Alpaydm
The MIT Press
Cambridge, Massachusetts
London, England
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© 2004 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.
MIT Press books may be purchased at special quantity discounts for business
or sales promotional use. For information, please email speciaLsales@mitpress.
mit.edu or write to Special Sales Department, The MIT Press, 5 Cambridge Cen-
ter, Cambridge, MA 02142.
Ubrary of Congress Control Number: 2004109627
ISBN: 0-262-01211-1 (hc)
Typeset in 10/13 Lucida Bright by the author using ~TEX 2E.
Printed and bound in the United States of America.
10987654321
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Contents
Series Foreword
xiii
Figures
xv
Tables
xxiii
Preface
xxv
Acknowledgments
xxvii
Notations
xxix
1 Introduction
1
1.1
What Is Machine Learning?
1
1.2
Examples of Machine Learning Applications
3
1.2.1 Learning Associations
3
1.2.2 Classification
4
1.2.3 Regression 8
1.2.4 Unsupervised Learning
10
1.2.5 Reinforcement Learning
11
1.3
Notes
12
1.4
Relevant Resources
14
1.5
Exercises
15
1.6
References
16
2 Supervised Learning
17
2.1
Learning a Class from Examples
17
2.2
Vapnik-Chervonenkis (VC) Dimension
22
2.3
Probably Approximately Correct (PAC) Learning
24
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vi
Contents
2.4
Noise
25
2.5
Learning Multiple Classes
27
2.6
Regression
29
2.7
Model Selection and Generalization
32
2.8
Dimensions of a Supervised Machine Learning Algorithm
35
2.9
Notes
36
2.10 Exercises
37
2.11 References
38
3 Bayesian Decision Theory
39
3.1
Introduction
39
3.2
Classification
41
3.3
Losses and Risks
43
3.4
Discriminant Functions
45
3.5
Utility Theory
46
3.6
Value of Information
47
3.7
Bayesian Networks
48
3.8
Influence Diagrams
55
3.9
Association Rules
56
3.10 Notes
57
3.11 Exercises
57
3.12 References
58
4 Parametric Methods
61
4.1
Introduction
61
4.2
Maximum Likelihood Estimation
62
4.2.1 Bernoulli Density 62
4.2.2 Multinomial Density 63
4.2.3 Gaussian (Normal) Density
64
4.3
Evaluating an Estimator: Bias and Variance
64
4.4
The Bayes' Estimator
67
4.5
Parametric Classification
69
4.6 Regression 73
4.7 Tuning Model Complexity: BiasjVariance Dilemma
76
4.8
Model Selection Procedures
79
4.9
Notes
82
4.10 Exercises
82
4.11 References
83
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