| 청구기호 |
Q325.5 .S475 2014 |
| 형태사항 |
1 online resource (xvi, 397 pages) : digital, PDF file(s).
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| 언어 |
English |
| 일반주기 |
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
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| 내용 |
Machine generated contents note: 1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity tradeoff; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.
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| 주제 |
Machine learning.
Algorithms.
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| 보유판 및 특별호 저록 |
Print version: 9781107057135
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| ISBN |
9781107298019 (ebook)
, 9781107057135 (hardback)
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| QR CODE |
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