Optimization for Machine Learning (Neural Information Processing series)

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Published by The MIT Press
ISBN 10: 8120347544 / ISBN 13: 9788120347540
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Synopsis: Optimization for Machine Learning- Prentice Hall-Paul H. Sra-2013-EDN-1

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Title: Optimization for Machine Learning (Neural ...
Publisher: The MIT Press
Binding: Paperback

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SRA/NOWOZIN/WRIGHT(Eds.)
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SRA/NOWOZIN/WRIGHT(Eds.)
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Book Description PHI Learning, 2013. Softcover. Condition: New. First edition. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today?s machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. 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. Printed Pages: 508. Seller Inventory # 73382

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Book Description 2013. Softcover. Condition: New. 1st edition. Brand NEW, Paperback International Edition. Black & White or color, Cover and ISBN same with similar contents as US editions. Standard delivery takes 5-9 business days by USPS/DHL with tracking number. Choose expedited shipping for superfast delivery 3-5 business days by UPS/DHL/FEDEX. We also ship to PO Box addresses but by Standard delivery and shipping charges will be extra. International Edition Textbooks may bear a label -Not for sale in the U.S. or Canada- etc. printed only to discourage U.S. students from obtaining an affordable copy. Legal to use despite any disclaimer on cover as per US court. No access code or CD included unless specified. In some instances, the international textbooks may have different exercises at the end of the chapters. Printed in English. We may ship the books from multiple warehouses across the globe, including India depending upon the availability of inventory storage. In case of orders from Europe, custom charges may comply by the relevant government authority and we are not liable for it. 100% Customer satisfaction guaranteed! Please feel free to contact us for any queries. Seller Inventory # LPBD1488203

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Sebastian Nowozin, Stephen J. Wright & Suvrit Sra (Eds)
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Book Description PHI Learning, 2013. Softcover. Condition: New. First edition. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of todayâs machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. 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 Problemâs 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. Printed Pages: 508. Seller Inventory # 73382

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Book Description 2013. Soft cover. Condition: New. International Edition. Book Condition: BRAND NEW. International/Eastern Economy Edition, Paperback/Softcover with SAME TITLE, AUTHOR AND EDITION as listed. ISBN and Cover design differs. **100% IDENTICAL CONTENTS as U.S Edition**. Standard Delivery within 7-14 business days ACROSS THE GLOBE. We can ship to PO Box, APO address in US. International Edition Textbooks may bear a label (Not for sale in the U.S. or Canada) or (For sale in Asia only) or similar restrictions- printed only to discourage students from obtaining an affordable copy. US Court has asserted your right to buy and use International edition. Access code/CD may not provided with these editions. We may ship the books from MULTIPLE WAREHOUSES ACROSS THE GLOBE including Asia depending upon the availability of inventory. Printed in English. Customer satisfaction guaranteed. Seller Inventory # ABECA3414

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