Foundations of machine learning [electronic resource]

• 作者： Mohri, Mehryar, author.
• 其他作者：
  • Rostamizadeh, Afshin, author.
  • Talwalkar, Ameet, author.
• 其他題名：
  • Adaptive computation and machine learning.
• 出版： Cambridge, Massachusetts : The MIT Press
• 版本：Second edition.
• 叢書名： Adaptive computation and machine learning
• 主題： Machine learning. , Computer algorithms. , Electronic books.
• ISBN： 9780262351362 (electronic bk.) 、 0262351366
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references and index. Introduction -- The PAC Learning Framework -- Rademacher Complexity and VC -- Dimension -- Model Selection -- Support Vector Machines -- Kernel Methods -- Boosting -- On -- Line Learning -- Multi -- Class Classification -- Ranking -- Regression -- Maximum Entropy Models -- Conditional Maximum Entropy Models -- Algorithmic Stability -- Dimensionality Reduction -- Learning Automata and Languages -- Reinforcement Learning -- Conclusion
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• 摘要註: 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. -- Provided by publisher
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