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ORCID17. ALT 2006: Barcelona, Spain
- José L. Balcázar, Philip M. Long, Frank Stephan:
Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings. Lecture Notes in Computer Science 4264, Springer 2006, ISBN 3-540-46649-5 - José L. Balcázar, Philip M. Long, Frank Stephan:
Editors' Introduction. 1-9
Invited Contributions
- Gunnar Rätsch
:
Solving Semi-infinite Linear Programs Using Boosting-Like Methods. 10-11 - Carole A. Goble, Óscar Corcho, Pinar Alper, David De Roure:
e-Science and the Semantic Web: A Symbiotic Relationship. 12 - Hans Ulrich Simon:
Spectral Norm in Learning Theory: Some Selected Topics. 13-27 - Padhraic Smyth:
Data-Driven Discovery Using Probabilistic Hidden Variable Models. 28 - Andrew Y. Ng:
Reinforcement Learning and Apprenticeship Learning for Robotic Control. 29-31
Regular Contributions
- Alp Atici, Rocco A. Servedio:
Learning Unions of omega(1)-Dimensional Rectangles. 32-47 - Nader H. Bshouty, Ehab Wattad:
On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle. 48-62 - Matti Kääriäinen:
Active Learning in the Non-realizable Case. 63-77 - Jorge Castro:
How Many Query Superpositions Are Needed to Learn? 78-92 - Frank J. Balbach, Thomas Zeugmann:
Teaching Memoryless Randomized Learners Without Feedback. 93-108 - Stephen A. Fenner, William I. Gasarch:
The Complexity of Learning SUBSEQ (A). 109-123 - Matthew de Brecht, Akihiro Yamamoto:
Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data. 124-138 - Sanjay Jain, Efim B. Kinber:
Learning and Extending Sublanguages. 139-153 - Sanjay Jain, Efim B. Kinber:
Iterative Learning from Positive Data and Negative Counterexamples. 154-168 - Sanjay Jain, Steffen Lange, Sandra Zilles:
Towards a Better Understanding of Incremental Learning. 169-183 - Nader H. Bshouty, Iddo Bentov:
On Exact Learning from Random Walk. 184-198 - Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman:
Risk-Sensitive Online Learning. 199-213 - Vladimir Vovk:
Leading Strategies in Competitive On-Line Prediction. 214-228 - Chamy Allenberg, Peter Auer, László Györfi, György Ottucsák:
Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring. 229-243 - Marcus Hutter:
General Discounting Versus Average Reward. 244-258 - Jan Poland:
The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection. 259-273 - Shane Legg:
Is There an Elegant Universal Theory of Prediction? 274-287 - Leonid Kontorovich, Corinna Cortes, Mehryar Mohri:
Learning Linearly Separable Languages. 288-303 - Kohei Hatano:
Smooth Boosting Using an Information-Based Criterion. 304-318 - Hsuan-Tien Lin, Ling Li:
Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice. 319-333 - Daniil Ryabko, Marcus Hutter:
Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence. 334-347 - Takeshi Shibata, Ryo Yoshinaka, Takashi Chikayama:
Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning. 348-362 - Andreas Maurer:
Unsupervised Slow Subspace-Learning from Stationary Processes. 363-377 - Atsuyoshi Nakamura:
Learning-Related Complexity of Linear Ranking Functions. 378-392
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