عنوان

Understanding machine learning :from theory to algorithms

عنوان اصلي

Understanding machine learning :from theory to algorithms

محل نشرو پخش و غیره

New York, NY, USA

نام ناشر، پخش کننده و غيره

Cambridge University Press

تاریخ نشرو بخش و غیره

2014.

نام خاص و کميت اثر

xvi, 397 pages : illustrations ; 26 cm

متن يادداشت

Includes bibliographical references )pages 385-393( and index

متن يادداشت

Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada

متن يادداشت

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; 01. Boosting; 11. Model selection and validation; 21. Convex learning problems; 31. Regularization and stability; 41. Stochastic gradient descent; 51. Support vector machines; 61. Kernel methods; 71. Multiclass, ranking, and complex prediction problems; 81. Decision trees; 91. Nearest neighbor; 02. Neural networks; Part III. Additional Learning Models: 12. Online learning; 22. Clustering; 32. Dimensionality reduction; 42. Generative models; 52. Feature selection and generation; Part IV. Advanced Theory: 62. Rademacher complexities; 72. Covering numbers; 82. Proof of the fundamental theorem of learning theory; 92. Multiclass learnability; 03. Compression bounds; 13. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra

عنصر شناسه ای

، Machine learning

عنصر شناسه ای

، Algorithms

عنصر شناسه ای

، COMPUTERS / Computer Vision & Pattern Recognition

شماره رده

عنصر شناسه اي

Shalev-Shwartz, Shai.

کد نقش

AU

عنصر شناسه اي

AU Ben-David, Shai

عنصر شناسه اي

TI