| Description |
1 online resource (417 pages) |
| Series |
Machine learning & pattern recognition series |
|
Chapman & Hall/CRC machine learning & pattern recognition series.
|
| Contents |
Front cover; Contents; Preface; Acknowledgments; Disclaimer; Chapter 1: Introduction; Chapter 2: Mathematical Preliminaries; Chapter 3: The Horse Race; Chapter 4: Elements of Utility Theory; Chapter 5: The Horse Race and Utility; Chapter 6: Select Methods for Measuring Model Performance; Chapter 7: A Utility-Based Approach toInformation Theory; Chapter 8: Utility-Based Model Performance Measurement; Chapter 9: Select Methods for Estimating Probabilistic Models; Chapter 10: A Utility-Based Approach to Probability Estimation; Chapter 11: Extensions; Chapter 12: Select Applications; References |
| Summary |
Statistical learning, particularly probabilistic model learning, has become increasingly important in recent years. Probabilistic models, however, are not usually studied for their own sake but for decision-making purposes. Written by authorities in the field, "Utility-Based Learning from Data" approaches the probabilistic modeling problem from the point of view of decision makers who operate in an uncertain environment, base their decisions on a probabilistic model, and build and assess this model accordingly. After reviewing utility theory, the book surveys and extends popular stat |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Machine learning.
|
|
Machine learning
|
| Form |
Electronic book
|
| Author |
Sandow, Sven
|
| ISBN |
9781420011289 |
|
1420011286 |
|
1322612285 |
|
9781322612287 |
|
