عنوان

Understanding machine learning :from theory to algorithms

Title Proper

Understanding machine learning :from theory to algorithms

Place of Publication, Distribution, etc.

New York, NY, USA

Name of Publisher, Distributor, etc.

Cambridge University Press

Date of Publication, Distribution, etc.

2014.

Specific Material Designation and Extent of Item

xvi, 397 pages : illustrations ; 26 cm

Text of Note

Includes bibliographical references )pages 385-393( and index

Text of Note

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

Text of Note

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

Entry Element

، Machine learning

Entry Element

، Algorithms

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، COMPUTERS / Computer Vision & Pattern Recognition

Class number

Entry Element

Shalev-Shwartz, Shai.

Relator Code

AU

AU Ben-David, Shai

TI