文档介绍：INTRODUCTION
TO
MACHINE LEARNING
AN EARLY DRAFT OF A PROPOSED
TEXTBOOK
Nils J. Nilsson
Robotics Laboratory
Department puter Science
Stanford University
Stanford, CA 94305
e-mail: ******@
November 3, 1998
Copyright
c 2005 Nils J. Nilsson
This material may not be copied, reproduced, or distributed without the
written permission of the copyright holder.
ii
Contents
1 Preliminaries 1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What is Machine Learning? . . . . . . . . . . . . . . . . . 1
Wellsprings of Machine Learning . . . . . . . . . . . . . . 3
Varieties of Machine Learning . . . . . . . . . . . . . . . . 4
Learning Input-Output Functions . . . . . . . . . . . . . . . . . . 5
Types of Learning . . . . . . . . . . . . . . . . . . . . . . 5
Input Vectors . . . . . . . . . . . . . . . . . . . . . . . . . 7
Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Training Regimes . . . . . . . . . . . . . . . . . . . . . . . 8
Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Performance Evaluation . . . . . . . . . . . . . . . . . . . 9
Learning Requires Bias . . . . . . . . . . . . . . . . . . . . . . . . 9
Sample Applications . . . . . . . . . . . . . . . . . . . . . . . . . 11
Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Bibliographical and Historical Remarks . . . . . . . . . . . . . . 13
2 Boolean Functions 15
Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Boolean Algebra . . . . . . . . . . . . . . . . . . . . . . . 15
Diagrammatic Representations . . . . . . . . . . . . . . . 16
Classes of Boolean Functions . . . . . . . . . . . . . . . . . . . . 17
Terms and Clauses . . . . . . . . . . . . . . . . . . . . . . 17
DNF Functions . . . . . . . . . . . . . .