Optimization for machine learning / edited by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright.

Contributor(s):

Material type: Text

TextLanguage: English Series: Neural information processing seriesPublication details: Cambridge, Mass. : MIT Press, c2012.Description: ix, 494 p. : ill. ; 26 cmISBN:

9788120347540
9780262016469 (hardcover : alk. paper)
026201646X (hardcover : alk. paper)

Subject(s):

DDC classification:

006.31 22 SRA

Contents:

Series Foreword. Preface. 1. Introduction: Optimization and Machine Learning 2. Convex Optimization with Sparsity-Inducing Norms 3. Interior-Point Methods for Large-Scale Cone Programming 4. Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey 5. First-Order Methods for Nonsmooth Convex Large-Scale Optimization I: General Purpose Methods 6. First-Order Methods for Nonsmooth Convex Large-Scale Optimization II: Utilizing Problem’s Structure 7. Cutting-Plane Methods in Machine Learning 8. Introduction to Dual Decomposition for Inference 9. Augmented Lagrangian Methods for Learning, Selecting, and Combining Features 10. The Convex Optimization Approach to Regret Minimization 11. Projected Newton-type Methods in Machine Learning 12. Interior-Point Methods in Machine Learning 13. The Tradeoffs of Large-Scale Learning 14. Robust Optimization in Machine Learning 15. Improving First and Second-Order Methods by Modeling Uncertainty 16. Bandit View on Noisy Optimization 17. Optimization Methods for Sparse Inverse Covariance Selection 18. A Pathwise Algorithms for Covariance Selection.

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
General Books	CUTN Central Library Generalia	Non-fiction	006.31 SRA (Browse shelf(Opens below))	Available	34122
General Books	CUTN Central Library Generalia	Non-fiction	006.31 SRA (Browse shelf(Opens below))	Available	34123
General Books	CUTN Central Library Generalia	Non-fiction	006.31 SRA (Browse shelf(Opens below))	Available	34124

Series Foreword. Preface.

1. Introduction: Optimization and Machine Learning

2. Convex Optimization with Sparsity-Inducing Norms

3. Interior-Point Methods for Large-Scale Cone Programming

4. Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey

5. First-Order Methods for Nonsmooth Convex Large-Scale Optimization

I: General Purpose Methods

6. First-Order Methods for Nonsmooth Convex Large-Scale Optimization

II: Utilizing Problem’s Structure

7. Cutting-Plane Methods in Machine Learning

8. Introduction to Dual Decomposition for Inference

9. Augmented Lagrangian Methods for Learning, Selecting, and Combining Features

10. The Convex Optimization Approach to Regret Minimization

11. Projected Newton-type Methods in Machine Learning

12. Interior-Point Methods in Machine Learning

13. The Tradeoffs of Large-Scale Learning

14. Robust Optimization in Machine Learning

15. Improving First and Second-Order Methods by Modeling Uncertainty

16. Bandit View on Noisy Optimization

17. Optimization Methods for Sparse Inverse Covariance Selection

18. A Pathwise Algorithms for Covariance Selection.

Includes bibliographical references.

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