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The 15th International Conference
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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
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On Kernels, Margins, and Low-dimensional Mappings
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Maria-Florina Balcan, Avrim Blum, and Santosh Vempala | ||
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Complexity of Pattern Classes and Lipschitz Property
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Amiran Ambroladze and John Shawe-Taylor | ||
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Universal Convergence of Semimeasures on Individual Random Sequences |
Marcus Hutter and Andrej Muchnik | ||
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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
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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 |
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A Criterion for the Existence of Predictive Complexity for Binary Games
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Yuri Kalnishkan, Vladimir Vovk, and Michael V. Vyugin | ||
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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
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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 |
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| 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 |
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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 | ||
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Learning Tree Languages from Positive Examples and Membership Queries |
Jérome Besombes and Jean-Yves Marion | ||
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Boosting Based on Divide and Merge |
Eiji Takimoto, Syuhei Koya, and Akira Maruoka | ||
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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 | ||
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Estimation of the Data Region Using Extreme-value Distributions
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Kazuho Watanabe and Sumio Watanabe |
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New Revision Algorithms |
Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, and György Turán | ||
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Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data |
Daniil Ryabko | ||
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Maximum Entropy Principle in Non-Probabilistic Setting |
V.P.Maslov and V.V.V'yugin | ||
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Learning Boolean Functions in AC0 on Attribute and Classification Noise |
Akinobu Miyata, Jun Tarui and Etsuji Tomita | ||
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Learning Content Sequencing in an Educational Environment According Student Needs |
Ana Iglesias, Paloma Martínez, Ricardo Aler and Fernando Fernández | ||
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Full Information Game with Gains and Losses |
Chamy Allenberg-Neeman and Benny Neeman | ||