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15. ALT 2004: Padova, Italy
- Shai Ben-David, John Case, Akira Maruoka:
Algorithmic Learning Theory, 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings. Lecture Notes in Computer Science 3244, Springer 2004, ISBN 978-3-540-23356-5
Invited Papers
- Ayumi Shinohara
:
String Pattern Discovery. 1-13 - Nicolò Cesa-Bianchi:
Applications of Regularized Least Squares to Classification Problems. 14-18 - Luc De Raedt, Kristian Kersting:
Probabilistic Inductive Logic Programming. 19-36 - Mikko Koivisto, Teemu Kivioja, Heikki Mannila, Pasi Rastas, Esko Ukkonen:
Hidden Markov Modelling Techniques for Haplotype Analysis. 37-52 - Pedro M. Domingos:
Learning, Logic, and Probability: A Unified View. 53
Inductive Inference
- Sanjay Jain, Efim B. Kinber:
Learning Languages from Positive Data and Negative Counterexamples. 54-68 - M. R. K. Krishna Rao:
Inductive Inference of Term Rewriting Systems from Positive Data. 69-82 - Eric Martin, Arun Sharma, Frank Stephan:
On the Data Consumption Benefits of Accepting Increased Uncertainty. 83-98 - Steffen Lange, Sandra Zilles:
Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space. 99-113
PAC Learning and Boostring
- Kohei Hatano, Osamu Watanabe:
Learning r-of-k Functions by Boosting. 114-126 - Eiji Takimoto, Syuhei Koya, Akira Maruoka:
Boosting Based on Divide and Merge. 127-141 - Akinobu Miyata, Jun Tarui, Etsuji Tomita:
Learning Boolean Functions in AC0 on Attribute and Classification Noise. 142-155
Statistical Supervised Learning
- Amos Fiat, Dmitry Pechyony:
Decision Trees: More Theoretical Justification for Practical Algorithms. 156-170 - Daniil Ryabko:
Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data. 171-180 - Amiran Ambroladze, John Shawe-Taylor
Complexity of Pattern Classes and Lipschitz Property. 181-193
Statistical Analysis of Unlabeld Data
- Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
On Kernels, Margins, and Low-Dimensional Mappings. 194-205 - Kazuho Watanabe, Sumio Watanabe:
Estimation of the Data Region Using Extreme-Value Distributions. 206-220 - Victor P. Maslov, Vladimir V. V'yugin:
Maximum Entropy Principle in Non-ordered Setting. 221-233 - Marcus Hutter
Universal Convergence of Semimeasures on Individual Random Sequences. 234-248
Online Sequence Prediction
- Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin:
A Criterion for the Existence of Predictive Complexity for Binary Games. 249-263 - Chamy Allenberg-Neeman, Benny Neeman:
Full Information Game with Gains and Losses. 264-278 - Marcus Hutter
Prediction with Expert Advice by Following the Perturbed Leader for General Weights. 279-293 - Jan Poland, Marcus Hutter
On the Convergence Speed of MDL Predictions for Bernoulli Sequences. 294-308
Approximate Optimization Algorithms
- Mark Herbster:
Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm. 309-323 - Hans Ulrich Simon
On the Complexity of Working Set Selection. 324-337 - Nikolas List:
Convergence of a Generalized Gradient Selection Approach for the Decomposition Method. 338-349 - Keisuke Yamazaki, Sumio Watanabe:
Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions. 350-364
Logic Based Learning
- Andrei A. Bulatov, Hubie Chen, Víctor Dalmau:
Learnability of Relatively Quantified Generalized Formulas. 365-379 - Yasuhito Mukouchi, Masako Sato:
Learning Languages Generated by Elementary Formal Systems and Its Application to SH Languages. 380-394 - Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán:
New Revision Algorithms. 395-409 - Marta Arias, Roni Khardon:
The Subsumption Lattice and Query Learning. 410-424
Query and Reinforcement Learning
- Satoshi Matsumoto, Takayoshi Shoudai:
Learning of Ordered Tree Languages with Height-Bounded Variables Using Queries. 425-439 - Jérôme Besombes, Jean-Yves Marion:
Learning Tree Languages from Positive Examples and Membership Queries. 440-453 - Ana Iglesias, Paloma Martínez, Ricardo Aler, Fernando Fernández:
Learning Content Sequencing in an Educational Environment According to Student Needs. 454-463
Tutorial Papers
- Toshiyuki Tanaka:
Statistical Learning in Digital Wireless Communications. 464-478 - Yoshiyuki Kabashima, Shinsuke Uda:
A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networks. 479-493 - Manfred Opper, Ole Winther:
Approximate Inference in Probabilistic Models. 494-504
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