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[ subject:"Algorithms." ]
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Understanding machine learning :from theory to algorithms /
紀錄類型:
書目-語言資料,印刷品 : 單行本
正題名/作者:
Understanding machine learning :/ Shai Shalev-Shwartz, Shai Ben-David.
其他題名:
from theory to algorithms /
作者:
Shalev-Shwartz, Shai.
其他作者:
Ben-David, Shai.
出版者:
New York, NY, USA :Cambridge University Press,2014.
面頁冊數:
xvi, 397 p. :ill. ;26 cm.
標題:
Algorithms. -
電子資源:
http://assets.cambridge.org/97811070/57135/cover/9781107057135.jpg
ISBN:
9781107057135 (hardback) :
ISBN:
1107057132 (hardback)
Understanding machine learning :from theory to algorithms /
Shalev-Shwartz, Shai.
Understanding machine learning :
from theory to algorithms /Shai Shalev-Shwartz, Shai Ben-David. - New York, NY, USA :Cambridge University Press,2014. - xvi, 397 p. :ill. ;26 cm.
Includes bibliographical references (p. 385-393) and index.
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.
"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 an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"--
ISBN: 9781107057135 (hardback) :NT1830
LCCN: 2014001779Subjects--Topical Terms:
149403
Algorithms.
LC Class. No.: Q325.5 / .S475 2014
Dewey Class. No.: 006.3/1
Understanding machine learning :from theory to algorithms /
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