| Description |
xviii, 538 pages : illustrations ; 25 cm |
| Contents |
1. Introduction -- 2. Problem statement, classical approaches, and adaptive learning -- 3. Regularization framework -- 4. Statistical learning theory -- 5. Nonlinear optimization strategies -- 6. Methods for data reduction and dimensionality reduction -- 7. Methods for regression -- 8. Classification -- 9. Support vector machines -- 10. Noninductive inference and alternative learning formulations -- 11. Concluding remarks -- App. A. Review of nonlinear optimization -- App. B. Eigenvalues and singular value decomposition |
| Summary |
"Learning from Data provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from, statistics, neural, networks, and pattern recognition can be applied - showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science." "Complete with over one hundred illustrations, case studies, examples, and chapter summaries, Learning from Data accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data."--BOOK JACKET |
| Notes |
Previous ed.: 1998 |
| Bibliography |
Includes bibliographical references (pages 519-531) and index |
| Subject |
Adaptive signal processing.
|
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Machine learning.
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Neural networks (Computer science)
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Fuzzy systems.
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| Author |
Mulier, Filip.
|
| LC no. |
2006038736 |
| ISBN |
9780471681823 (cloth) |
|
0471681822 (cloth) |
|
