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Author Mohri, Mehryar, author.

Title Foundations of machine learning / Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.

Publication Info. Cambridge, Massachusetts : The MIT Press, [2018]

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 University of Saint Joseph: Pope Pius XII Library - Internet  WORLD WIDE WEB E-BOOK MIT    Downloadable
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Edition Second edition.
Description 1 online resource (xv, 486 pages) : illustrations (some color).
Series Adaptive computation and machine learning
Adaptive computation and machine learning.
Bibliography Includes bibliographical references and index.
Contents 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
Summary 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
Local Note MIT Press DTL OA MIT Titles
Subject Machine learning.
Computer algorithms.
Computer algorithms. (OCoLC)fst00872010
Machine learning. (OCoLC)fst01004795
Added Author Rostamizadeh, Afshin, author.
Talwalkar, Ameet, author.
Other Form: Print version: Mohri, Mehryar. Foundations of machine learning. Second edition. Cambridge, Massachusetts : The MIT Press, [2018] 0262039400 9780262039406 (DLC) 2018022812 (OCoLC)1041560990
ISBN 9780262039406 hardcover ; alkaline paper
0262039400 hardcover ; alkaline paper
Standard No. 99979017014
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