| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045956960 | |
| 005 | 20181010114734 | |
| 008 | 181010s2014 nyua b 001 0 eng d | |
| 010 | ▼a 2014001779 | |
| 020 | ▼a 9781107057135 (hardback) | |
| 020 | ▼a 1107057132 (hardback) | |
| 035 | ▼a (KERIS)REF000017964916 | |
| 040 | ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a Q325.5 ▼b .S475 2014 |
| 082 | 0 0 | ▼a 006.3/1 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b S528u | |
| 100 | 1 | ▼a Shalev-Shwartz, Shai. |
| 245 | 1 0 | ▼a Understanding machine learning : ▼b from theory to algorithms / ▼c Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada. |
| 260 | ▼a New York, NY, USA : ▼b Cambridge University Press, ▼c c2014. | |
| 300 | ▼a xvi, 397 p. : ▼b ill. ; ▼c 26 cm. | |
| 504 | ▼a Includes bibliographical references (p. 385-393) and index. | |
| 505 | 8 | ▼a 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. |
| 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"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Machine learning. |
| 650 | 0 | ▼a Algorithms. |
| 700 | 1 | ▼a Ben-David, Shai. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 S528u | 등록번호 121246195 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
목차
Introduction -- I. Foundations -- A gentle start -- A formal learning model -- Learning via uniform convergence -- The bias-complexity tradeoff -- The VC-dimension -- Nonuniform learnability -- The runtime of learning -- II. From Theory to Algorithms -- Linear predictors -- Boosting -- Model selection and validation -- Convex learning problems -- Regularization and stability -- Stochastic gradient descent -- Support vector machines -- Kernel methods -- Multiclass, ranking, and complex prediction problems -- Decision trees -- Nearest neighbor -- Neural networks -- III. Additional Learning Models -- Online learning -- Clustering -- Dimensionality reduction -- Generative models -- Feature selection and generation -- IV. Advanced Theory -- Rademacher complexities -- Covering numbers -- Proof of the fundamental theorem of learning theory -- Multiclass learnability -- Compression bounds -- PAC-Bayes.