000			03250nam a22003137a 4500
003			OSt
005			20260401111705.0
008			260401s2014 nyua frb 001 0 eng
010			_a 2014001779
020			_a9781107057135
035			_a18053648
040			_aDLC _beng _cDLC _erda _dDLC _dclrauoh
082	0	0	_a006.31
100	1		_aShalev-Shwartz, Shai. _eauthor.
245	1	0	_aUnderstanding machine learning : _bfrom theory to algorithms / _cShai Shalev-Shwartz, Shai Ben-David
260			_aNew York, NY, USA : _bCambridge University Press, _c2014
300			_axvi, 397 pages : _billustrations.
336			_atexto _btxt _2rdacontent
337			_ano mediado _bn _2rdamedia
338			_avolumen _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 385-393) and index.
505	8		_a1. 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.
520			_a"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"-- _cProvided by publisher.
650		0	_aMachine learning.
650		0	_aAlgorithms.
650		7	_aCOMPUTERS / Computer Vision & Pattern Recognition. _2bisacsh
700	1		_aBen-David, Shai. _eauthor.
942			_2ddc _cBOOK _n0
999			_c7009 _d7009