Understanding machine learning : from theory to algorithms
La quatrième de couverture indique : "Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretica...
Shranjeno v:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre papier |
| Jezik: | Anglais |
| Izdano: |
New York :
Cambridge University Press
C 2014.
|
| Teme: | |
| Sporočilo: |
Autre tirage: 2018 |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Understanding machine learning, from theory to algorithms, Shai Shalev-Shwartz,... Shai Ben-David,..., 2014, New York, NY, Cambridge University Press, Institute of Mathematical Statistics Textbooks, 978-1-107-29801-9 |
Kazalo:
- 1. Introduction;
- Part I. Foundations:
- 2. A gentle start;
- 3. A formal learning model;
- 4. Learning via uniform convergence;
- 5. The bias-complexity trade-off;
- 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.