|
|
|
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
| ISBN | 0070428077 | | Pages | 432 | | ISBN13 | 9780070428072
(What's this?) | | Volumes | 1 | | Publisher | McGraw-Hill Education - Europe | | Weight (grammes) | 707 | | Imprint | McGraw Hill Higher Education | | Published in | London | | Format | Hardback | | Series title | McGraw-Hill Series in Computer Science | | Publication date | 01 Apr 1997 | | Height (mm) | 243 | | Library of Congress | Q325.5.M58 | | Width (mm) | 160 | | DEWEY | 006.31 | | Spine width (mm) | 25 | | DEWEY edition | DC21 | | Academic level | Tertiary education |
|
| |
| | | Preface | | | | | | Acknowledgments | | | | 1 | | Introduction | | 1 | | 2 | | Concept Learning and the General-to-Specific Ordering | | 20 | | 3 | | Decision Tree Learning | | 52 | | 4 | | Artificial Neural Networks | | 81 | | 5 | | Evaluating Hypotheses | | 128 | | 6 | | Bayesian Learning | | 154 | | 7 | | Computational Learning Theory | | 201 | | 8 | | Instance-Based Learning | | 230 | | 9 | | Genetic Algorithms | | 249 | | 10 | | Learning Sets of Rules | | 274 | | 11 | | Analytical Learning | | 307 | | 12 | | Combining Inductive and Analytical Learning | | 334 | | 13 | | Reinforcement Learning | | 367 | | | | Appendix: Notation | | 391 | | | | Author Index | | 394 | | | | Subject Index | | 400 |
|
|
|
|
|
Enlarge
