| Description |
xv, 246 pages : illustrations ; 25 cm |
| Series |
Wiley series on methods and applications in data mining |
|
Wiley series on methods and applications in data mining.
|
| Contents |
1. What is knowledge discovery? -- 2. Knowledge discovery environments -- 3. Describing data mathematically -- 4. Linear decision surfaces and functions -- 5. Perception learning -- 6. Maximum-margin classifiers -- 7. Support vector machines -- 8. Implementation -- 9. Evaluating what has been learned -- 10. Elements of statistical learning theory -- 11. Multiclass classification -- 12. Regression with support vector machines -- 13. Novelty detection -- App. A. Notation -- App. B. Tutorial introduction to R |
| Summary |
"This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas."--BOOK JACKET |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Support vector machines.
|
|
Data mining.
|
|
Machine learning.
|
|
Computer algorithms.
|
| LC no. |
2009011948 |
| ISBN |
9780470371923 cloth |
|
0470371927 cloth |
|
