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英文原版 Foundations of Machine Learning, second edition 机器学习基础精装第二版英文版进口英语原版书籍

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作者: Mohri, Mehryar
出版社: The MIT Press
ISBN: 9780262039406

出版时间: 2018-12
装帧: 精装

作者: Mohri, Mehryar
出版社: The MIT Press

ISBN: 9780262039406
出版时间: 2018-12

装帧: 精装

售价 568.00

定价￥568.00

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上书时间2024-03-19

数量: 库存10件

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商品分类：

外文古旧书 > 英文书 > 综合性图书

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YBYDA-4785

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内容摘要
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
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