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Suvrit Sra is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

Sebastian Nowozin is a Postdoctoral Researcher at Microsoft Research, Cambridge, UK.

Stephen J. Wright is Professor in the Computer Sciences Department at the University of Wisconsin, Madison.

出版者:The MIT Press

作者:Suvrit Sra

出品人:

頁數:512

译者:

出版時間:2011-9-30

價格:USD 50.00

裝幀:Hardcover

isbn號碼:9780262016469

叢書系列:

圖書標籤:

機器學習
Optimization
優化
MachineLearning
數學
計算機科學
最優化
計算機

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The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.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.