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...
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre papier |
| Sprog: | Anglais |
| Udgivet: |
New York :
Cambridge University Press
C 2014.
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| Fag: | |
| Kommentar: |
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 |
| LEADER | 03493nam a22003017a 4500 | ||
|---|---|---|---|
| 001 | 918071 | ||
| 008 | 140804t20142014xxe ||| |||| 00| 0 eng d | ||
| 009 | PPN179880268 | ||
| 020 | |a 9781107057135 (rel.) | ||
| 024 | |a 9781107057135 | ||
| 041 | 0 | |a eng | |
| 082 | |a 006.31 | ||
| 084 | |a 68-01. 2020 | ||
| 084 | |a 68T05. 2020 | ||
| 100 | 1 | |a Shalev-Shwartz, Shai. | |
| 245 | 1 | 0 | |a Understanding machine learning : |b from theory to algorithms |c Shai Shalev-Shwartz,... Shai Ben-David,... |
| 260 | |a New York : |b Cambridge University Press. | ||
| 260 | |c C 2014. | ||
| 300 | |a 1 vol. (xvi-397 p.) : |b ill., couv. ill. en coul. ; |c 26 cm. | ||
| 500 | |a Autre tirage: 2018 | ||
| 504 | |a Bibliogr. p. 385-393. Index | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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 theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering." | ||
| 650 | |a Apprentissage automatique | ||
| 650 | |a Algorithmes | ||
| 700 | 1 | |a Ben-David, Shai. |4 aut | |
| 776 | 0 | |0 231141513 |t Understanding machine learning |o from theory to algorithms |f Shai Shalev-Shwartz,... Shai Ben-David,... |d 2014 |c New York, NY |n Cambridge University Press |s Institute of Mathematical Statistics Textbooks |z 978-1-107-29801-9 | |
| 997 | |0 918071 |1 Livre papier |a Ressource papier |c 0/Orléans/ |c 1/Orléans/IDP/ |z Orléans, IDP, 9201 SHA | ||