Foundations of machine learning

By: Mohri,MehryarMaterial type: TextTextPublication details: Massachusetts: MIT Press, London [c2012]Description: 411 pISBN: 9780262018258
Contents:
1 Introduction 2 The PAC Learning Framework 3 Rademacher Complexity and VC Dimension 4 Support Vector Machines 5 Kernel Methods 6 Boosting 7 OnLine Learning 8 MultiClass Classification 9 Ranking 10 Regression 11 Algorithmic Stability 12 Dimensionality Reduction 13 Learning Automata and Languages 14 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.
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Item type Current library Collection Shelving location Call number Status Notes Date due Barcode Item holds
Book Book ICTS
General Sc Rack No 3 Q325.5 (Browse shelf (Opens below)) Available Billno:95020; Billdate: 2016-07-28 00259
Total holds: 0

1 Introduction
2 The PAC Learning Framework
3 Rademacher Complexity and VC Dimension
4 Support Vector Machines
5 Kernel Methods
6 Boosting
7 OnLine Learning
8 MultiClass Classification
9 Ranking
10 Regression
11 Algorithmic Stability
12 Dimensionality Reduction
13 Learning Automata and Languages
14 Reinforcement Learning
Conclusion

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.

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