The 15th International Conference
Algorithmic Learning Theory

Padova University, Padova, Italy
October 2 - 5, 2004


     The following papers have been accepted for ALT 2004.
     There is no particular order in the list.

     Please keep in mind that we need your final version until

                July 25, 2004. 

Relative Loss Bounds and Polynomial-time Predictions for the K-LMS-NET Algorithm Mark Herbster

On Kernels, Margins, and Low-dimensional Mappings

Maria-Florina Balcan, Avrim Blum, and Santosh Vempala
Complexity of Pattern Classes and Lipschitz Property

Amiran Ambroladze and John Shawe-Taylor
Universal Convergence of Semimeasures on Individual Random Sequences

Marcus Hutter and Andrej Muchnik
On the Complexity of Working Set Selection

Hans Ulrich Simon
Learnability of Relatively Quantified Generalized Formulas Andrei Bulatov, Hubie Chen, and Victor Dalmau

Prediction with Expert Advice by Following the Perturbed Leader for General Weights

Marcus Hutter and Jan Poland
Learning Languages from Positive Data and Negative Counterexamples

Sanjay Jain and Efim Kinber
Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions Keisuke Yamazaki and Sumio Watanabe

A Criterion for the Existence of Predictive Complexity for Binary Games

Yuri Kalnishkan, Vladimir Vovk, and Michael V. Vyugin
On the Convergence Speed of MDL Predictions for Bernoulli Sequences

Jan Poland and Marcus Hutter
Convergence of a Generalized Gradient Selection Approach for the Decomposition Method Nikolas List

Comparison of Query Learning and Gold-style Learning in Dependence of the Hypothesis Space

Steffen Lange Sandra Zilles
Learning of Ordered Tree Languages with Height-Bounded Variables using Queries Satoshi Matsumoto and Takayoshi Shoudai

Learning r-of-k functions by Boosting

Kohei Hatano and Osamu Watanabe
Decision Trees: More Theoretical Justification for Practical Algorithms Amos Fiat and Dmitry Pechyony

Learning Languages generated by Elementary Formal Systems and its Application to SH Languages

Yasuhito Mukouchi and Masako Sato
Inductive Inference of Term Rewriting Systems from Positive Data

M. R. K. Krishna Rao
Learning Tree Languages from Positive Examples and Membership Queries

Jérome Besombes and Jean-Yves Marion
Boosting Based on Divide and Merge

Eiji Takimoto, Syuhei Koya, and Akira Maruoka
On the Data Consumption Benefits of Accepting Increased Uncertainty

Eric Martin, Arun Sharma, Frank Stephan
The Subsumption Lattice and Query Learning

Marta Arias and Roni Khardon
Estimation of the Data Region Using Extreme-value Distributions

Kazuho Watanabe and Sumio Watanabe

New Revision Algorithms

Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, and György Turán
Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data

Daniil Ryabko
Maximum Entropy Principle in Non-Probabilistic Setting

V.P.Maslov and V.V.V'yugin
Learning Boolean Functions in AC0 on Attribute and Classification Noise

Akinobu Miyata, Jun Tarui and Etsuji Tomita
Learning Content Sequencing in an Educational Environment According Student Needs

Ana Iglesias, Paloma Martínez, Ricardo Aler and Fernando Fernández
Full Information Game with Gains and Losses

Chamy Allenberg-Neeman and Benny Neeman