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首页Machine Learning in Action 原版PDF by Harrington
This book sets out to introduce people to important machine learning algorithms. Tools and applications using these algorithms are introduced to give the reader an idea of how they are used in practice today. A wide selection of machine learning books is available, which discuss the mathematics, but discuss little of how to program the algorithms. This book aims to be a bridge from algorithms presented in matrix form to an actual functioning program. With that in mind, please note that this book is heavy on code and light on mathematics
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MANNING
Peter Harrington
IN ACTION
Machine Learning in Action
PETER HARRINGTON
MANNING
Shelter Island
For online information and ordering of this and other Manning books, please visit
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©2012 by Manning Publications Co. All rights reserved.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in
any form or by means electronic, mechanical, photocopying, or otherwise, without prior written
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Publications was aware of a trademark claim, the designations have been printed in initial caps
or all caps.
Recognizing the importance of preserving what has been written, it is Manning’s policy to have
the books we publish printed on acid-free paper, and we exert our best efforts to that end.
Recognizing also our responsibility to conserve the resources of our planet, Manning books are
printed on paper that is at least 15 percent recycled and processed without the use of elemental
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Manning Publications Co.Development editor:Jeff Bleiel
20 Baldwin Road Technical proofreaders: Tricia Hoffman, Alex Ott
PO Box 261 Copyeditor: Linda Recktenwald
Shelter Island, NY 11964 Proofreader: Maureen Spencer
Typesetter: Gordan Salinovic
Cover designer: Marija Tudor
ISBN 9781617290183
Printed in the United States of America
1 2 3 4 5 6 7 8 9 10 – MAL – 17 16 15 14 13 12
vii
brief contents
P
ART
1 C
LASSIFICATION
...............................................................1
1
■
Machine learning basics 3
2
■
Classifying with k-Nearest Neighbors 18
3
■
Splitting datasets one feature at a time: decision trees 37
4
■
Classifying with probability theory: naïve Bayes 61
5
■
Logistic regression 83
6
■
Support vector machines 101
7
■
Improving classification with the AdaBoost
meta-algorithm 129
P
ART
2 F
ORECASTING
NUMERIC
VALUES
WITH
REGRESSION
.............. 151
8
■
Predicting numeric values: regression 153
9
■
Tree-based regression 179
P
ART
3 U
NSUPERVISED
LEARNING
...............................................205
10
■
Grouping unlabeled items using k-means clustering 207
11
■
Association analysis with the Apriori algorithm 224
12
■
Efficiently finding frequent itemsets with FP-growth 248
BRIEF
CONTENTS
viii
P
ART
4 A
DDITIONAL
TOOLS
.......................................................267
13
■
Using principal component analysis to simplify data 269
14
■
Simplifying data with the singular value
decomposition 280
15
■
Big data and MapReduce 299
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