| Description |
1 online resource |
| Series |
Lecture notes in artificial intelligence ; 7568 |
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Lecture notes in computer science, 0302-9743 |
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LNCS sublibrary. SL 7, Artificial intelligence |
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Lecture notes in computer science. Lecture notes in artificial intelligence ; 7568
|
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Lecture notes in computer science.
|
|
LNCS sublibrary. SL 7, Artificial intelligence.
|
| Contents |
Editors' Introduction / Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis and Thomas Zeugmann -- Declarative Modeling for Machine Learning and Data Mining / Luc De Raedt -- Learnability beyond Uniform Convergence / Shai Shalev-Shwartz -- Some Rates of Convergence for the Selected Lasso Estimator / Pascal Massart and Caroline Meynet -- Recent Developments in Pattern Mining / Toon Calders -- Exploring Sequential Data / Gilbert Ritschard -- Enlarging Learnable Classes / Sanjay Jain, Timo Kötzing and Frank Stephan -- Confident and Consistent Partial Learning of Recursive Functions / Ziyuan Gao and Frank Stephan -- Automatic Learning from Positive Data and Negative Counterexamples / Sanjay Jain and Efim Kinber -- Regular Inference as Vertex Coloring / Christophe Costa Florêncio and Sicco Verwer -- Sauer's Bound for a Notion of Teaching Complexity / Rahim Samei, Pavel Semukhin, Boting Yang and Sandra Zilles -- On the Learnability of Shuffle Ideals / Dana Angluin, James Aspnes and Aryeh Kontorovich -- New Analysis and Algorithm for Learning with Drifting Distributions / Mehryar Mohri and Andres Muñoz Medina -- On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples / Shai Ben-David and Ruth Urner -- Efficient Protocols for Distributed Classification and Optimization / Hal Daumé III, Jeff M. Phillips, Avishek Saha and Suresh Venkatasubramanian |
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The Safe Bayesian / Learning the Learning Rate via the Mixability Gap / Peter Grünwald -- Data Stability in Clustering: A Closer Look / Lev Reyzin -- Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis / Emilie Kaufmann, Nathaniel Korda and Rémi Munos -- Regret Bounds for Restless Markov Bandits / Ronald Ortner, Daniil Ryabko, Peter Auer and Rémi Munos -- Minimax Number of Strata for Online Stratified Sampling Given Noisy Samples / Alexandra Carpentier and Rémi Munos -- Weighted Last-Step Min-Max Algorithm with Improved Sub-logarithmic Regret / Edward Moroshko and Koby Crammer -- Online Prediction under Submodular Constraints / Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto and Kiyohito Nagano -- Lower Bounds on Individual Sequence Regret / Eyal Gofer and Yishay Mansour -- A Closer Look at Adaptive Regret / Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov and Vladimir Vovk -- Partial Monitoring with Side Information / Gábor Bartók and Csaba Szepesvári -- PAC Bounds for Discounted MDPs / Tor Lattimore and Marcus Hutter -- Buy Low, Sell High / Wouter M. Koolen and Vladimir Vovk -- Kernelization of Matrix Updates, When and How? / Manfred K. Warmuth, Wojciech Kotłowski and Shuisheng Zhou -- Predictive Complexity and Generalized Entropy Rate of Stationary Ergodic Processes / Mrinalkanti Ghosh and Satyadev Nandakumar |
| Summary |
This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning |
| Analysis |
Computer science |
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Computer software |
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Logic design |
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Artificial intelligence |
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Optical pattern recognition |
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Mathematical Logic and Formal Languages |
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Algorithm Analysis and Problem Complexity |
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Computation by Abstract Devices |
| Bibliography |
Includes bibliographical references and author index |
| Notes |
English |
| Subject |
Computer algorithms -- Congresses
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Machine learning -- Congresses
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Informatique.
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Computer algorithms
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Machine learning
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Algorithmische Lerntheorie
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| Genre/Form |
Conference papers and proceedings
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Software.
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| Form |
Electronic book
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| Author |
Bshouty, Nader H.
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| ISBN |
9783642341069 |
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3642341063 |
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3642341055 |
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9783642341052 |
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