, Solution Manual to accompany
Pattern Classification (2nd ed.)
by R. O. Duda, P. E. Hart and D. G. Stork
David G. Stork
Copyright 2001. All rights reserved.
This Manual is for the sole use of designated educators
and must not be distributed to students
except in short, isolated portions and in conjunction
with the use of Pattern Classification (2nd ed.)
June 18, 2003
,Contents
Preface
1 Introduction 5
2 Bayesian decision theory 7
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3 Maximum likelihood and Bayesian parameter estimation 77
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4 Nonparametric techniques 131
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
5 Linear discriminant functions 177
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
6 Multilayer neural networks 219
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
7 Stochastic methods 255
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
8 Nonmetric methods 277
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
9 Algorithm-independent machine learning 295
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
10 Unsupervised learning and clustering 305
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
Sample final exams and solutions 357
3
, 4 CONTENTS
Worked examples 415
Errata and ammendations in the text 417
First and second printings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
Fifth printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
Pattern Classification (2nd ed.)
by R. O. Duda, P. E. Hart and D. G. Stork
David G. Stork
Copyright 2001. All rights reserved.
This Manual is for the sole use of designated educators
and must not be distributed to students
except in short, isolated portions and in conjunction
with the use of Pattern Classification (2nd ed.)
June 18, 2003
,Contents
Preface
1 Introduction 5
2 Bayesian decision theory 7
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3 Maximum likelihood and Bayesian parameter estimation 77
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4 Nonparametric techniques 131
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
5 Linear discriminant functions 177
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
6 Multilayer neural networks 219
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
7 Stochastic methods 255
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
8 Nonmetric methods 277
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
9 Algorithm-independent machine learning 295
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
10 Unsupervised learning and clustering 305
Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
Computer Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
Sample final exams and solutions 357
3
, 4 CONTENTS
Worked examples 415
Errata and ammendations in the text 417
First and second printings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
Fifth printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
