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Foundations of machine learning

Author: Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
Publisher: Cambridge, MA : MIT Press, ©2012.
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.
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Genre/Form: Electronic books
Electronic book
Additional Physical Format: Print version:
Mohri, Mehryar.
Foundations of machine learning.
Cambridge, MA : MIT Press, ©2012
(DLC) 2012007249
(OCoLC)782251805
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
ISBN: 9780262305662 0262305666
OCLC Number: 809846149
Description: 1 online resource (xii, 414 pages) : illustrations.
Contents: ""Contents""; ""Preface""; ""1 Introduction""; ""1.1 Applications and problems""; ""1.2 Definitions and terminology""; ""1.3 Cross-validation""; ""1.4 Learning scenarios""; ""1.5 Outline""; ""2 The PAC Learning Framework""; ""2.1 The PAC learning model""; ""2.2 Guarantees for finite hypothesis sets�consistent case""; ""2.3 Guarantees for finite hypothesis sets�inconsistent case""; ""2.4 Generalities""; ""2.5 Chapter notes""; ""2.6 Exercises""; ""3 Rademacher Complexity and VC Dimension""; ""3.1 Rademacher complexity""; ""3.2 Growth function""; ""3.3 VC-dimension""; ""3.4 Lower bounds"" ""3.5 Chapter notes""""3.6 Exercises""; ""4 Support Vector Machines""; ""4.1 Linear classification""; ""4.2 SVMs�separable case""; ""4.3 SVMs�non-separable case""; ""4.4 Margin theory""; ""4.5 Chapter notes""; ""4.6 Exercises""; ""5 Kernel Methods""; ""5.1 Introduction""; ""5.2 Positive definite symmetric kernels""; ""5.3 Kernel-based algorithms""; ""5.4 Negative definite symmetric kernels""; ""5.5 Sequence kernelsThe examples given in the previous""; ""5.6 Chapter notes""; ""5.7 Exercises""; ""6 Boosting""; ""6.1 Introduction""; ""6.2 AdaBoost""; ""6.3 Theoretical results"" ""6.4 Discussion""""6.5 Chapter notes""; ""6.6 Exercises""; ""7 On-Line Learning""; ""7.1 Introduction""; ""7.2 Prediction with expert advice""; ""7.3 Linear classification""; ""7.4 On-line to batch conversion""; ""7.5 Game-theoretic connection""; ""7.6 Chapter notes""; ""7.7 Exercises""; ""8 Multi-Class Classification""; ""8.1 Multi-class classification problem""; ""8.2 Generalization bounds""; ""8.3 Uncombined multi-class algorithms""; ""8.4 Aggregated multi-class algorithms""; ""8.5 Structured prediction algorithms""; ""8.6 Chapter notes""; ""8.7 Exercises""; ""9 Ranking"" ""9.1 The problem of ranking""""9.2 Generalization bound""; ""9.3 Ranking with SVMs""; ""9.4 RankBoost""; ""9.5 Bipartite ranking""; ""9.6 Preference-based setting""; ""9.7 Discussion""; ""9.8 Chapter notes""; ""9.9 Exercises""; ""10 Regression""; ""10.1 The problem of regression""; ""10.2 Generalization bounds""; ""10.3 Regression algorithms""; ""10.4 Chapter notes""; ""10.5 Exercises""; ""11 Algorithmic Stability""; ""11.1 Definitions""; ""11.2 Stability-based generalization guarantee""; ""11.3 Stability of kernel-based regularization algorithms""; ""11.4 Chapter notes""; ""11.5 Exercises"" ""12 Dimensionality Reduction""""12.1 Principal Component Analysis""; ""12.2 Kernel Principal Component Analysis (KPCA)""; ""12.3 KPCA and manifold learning""; ""12.4 Johnson-Lindenstrauss lemma""; ""12.5 Chapter notes""; ""12.6 Exercises""; ""13 Learning Automata and Languages""; ""13.1 Introduction""; ""13.2 Finite automata""; ""13.3 Efficient exact learning""; ""13.4 Identification in the limit""; ""13.5 Chapter notes""; ""13.6 Exercises""; ""14 Reinforcement Learning""; ""14.1 Learning scenario""; ""14.2 Markov decision process model""; ""14.3 Policy""; ""14.4 Planning algorithms""
Series Title: Adaptive computation and machine learning.
Responsibility: Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.

Abstract:

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.  Read more...

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