The 17th International Conference
Algorithmic Learning Theory

Barcelona, Spain
October 7-10, 2006


     The following papers have been accepted for ALT 2006.
     There is no particular order in the list.

     Please keep in mind that we need your final version until

                July 27, 2006. 

Learning and Extending Sublanguages Sanjay Jain and Efim Kinber

Iterative Learning from Positive Data and Negative Counterexamples Sanjay Jain and Efim Kinber

On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle Nader H. Bshouty and Ehab Wattad

General Discounting versus Average Reward Marcus Hutter

Learning Unions of ω(1)-Dimensional Rectangles Alp Atici and Rocco A. Servedio

The Complexity of Learning SUBSEQ(A) Stephen Fenner and William Gasarch

How Many Query Superpositions Are Needed to Learn? Jorge Castro

The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection Jan Poland

Unsupervised Slow Subspace-Learning from Stationary Processes Andreas Maurer

Asymptotic Learnability of Reinforcement Problems
with Arbitrary Dependence
Daniil Ryabko and Marcus Hutter

Smooth Boosting Using an Information-Based Criterion Kohei Hatano

On Exact Learning from Random Walk Nader H. Bshouty and Iddo Bentov

Risk-Sensitive Online Learning Eyal Even-Dar, Michael Kearns, and Jennifer Wortman

Leading Strategies in Competitive on-line Learning Vladimir Vovk

Active Learning in the Non-realizable Case Matti Kääriäinen

Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages From Positive Data Matthew de Brecht and Akihiro Yamamoto

Learning-Related Complexity of Linear Ranking Functions Atsuyoshi Nakamura

Hannan Consistency in on-line Learning in Case of Unbounded Losses under Partial Monitoring Chamy Allenberg, Peter Auer, László Györfi and György Ottucsák

Teaching Memoryless Randomized Learners Without Feedback Frank J. Balbach and Thomas Zeugmann

Towards a Better Understanding of Incremental Learning Sanjay Jain, Steffen Lange and Sandra Zilles

Is there an Elegant Universal Theory of Prediction? Shane Legg

Probabilistic Generalization of Simple Grammars and
Its Application to Reinforcement Learning
Takeshi Shibata, Ryo Yoshinaka and Takashi Chikayama

Learning Linearly Separable Languages Leonid Kontorovich, Corinna Cortes and Mehryar Mohri

Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice Hsuan-Tien Lin and Ling Li

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