Foundations of Machine Learning, 2/e (Hardcover)

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

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商品描述

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.

商品描述(中文翻譯)

一本研究機器學習算法分析和理論的研究生級別教材的新版本。

這本書是一本機器學習的綜合介紹,可作為研究生學生的教材和研究人員的參考書。它涵蓋了機器學習中的基礎現代主題,同時提供了討論和證明算法所需的理論基礎和概念工具。它還描述了這些算法應用的幾個關鍵方面。作者的目標是提供新穎的理論工具和概念,同時對相對高級的主題給出簡潔的證明。

《機器學習基礎》在其對算法分析和理論的關注上是獨特的。前四章為後續章節奠定了理論基礎;後續章節大多是獨立的。涵蓋的主題包括可能近似正確(PAC)學習框架;基於Rademacher複雜度和VC維度的泛化界限;支持向量機(SVM);核方法;提升;在線學習;多類別分類;排名;回歸;算法穩定性;降維;學習自動機和語言;以及強化學習。每章結束時都有一組練習題。附錄提供了額外的材料,包括簡潔的概率回顧。

這本第二版增加了三章,分別是模型選擇、最大熵模型和條件熵模型。附錄中的新材料包括一個關於Fenchel對偶性的重要部分,對集中不等式的涵蓋範圍進行了擴展,並新增了一個關於信息理論的全新內容。超過一半的練習題是本版新增的。