Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9925)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
Included in the following conference series:
Conference proceedings info: ALT 2016.
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Table of contents (24 papers)
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Front Matter
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Error Bounds, Sample Compression Schemes
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Front Matter
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Statistical Learning Theory, Evolvability
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Front Matter
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Exact and Interactive Learning, Complexity of Teaching Models
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Front Matter
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Inductive Inference
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Front Matter
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Online Learning
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Front Matter
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Other volumes
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Algorithmic Learning Theory
Keywords
- active learning
- inductive inference
- online learning algorithms
- reinforcement learning
- sequential decision making
- adversary models
- boolean function learning
- clustering
- evolutionary algorithms
- interactive learning
- local search
- models of learning
- online learning theory
- optimization
- perceptron
- query learning
- sample complexity and generalization bounds
- structured prediction
- semi-supervised learning
- structured prediction
- unsupervised learning
About this book
This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
Editors and Affiliations
Bibliographic Information
Book Title: Algorithmic Learning Theory
Book Subtitle: 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings
Editors: Ronald Ortner, Hans Ulrich Simon, Sandra Zilles
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-46379-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Softcover ISBN: 978-3-319-46378-0Published: 21 September 2016
eBook ISBN: 978-3-319-46379-7Published: 12 October 2016
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XIX, 371
Number of Illustrations: 21 b/w illustrations
Topics: Artificial Intelligence, Theory of Computation, Data Mining and Knowledge Discovery, Pattern Recognition