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ORCID14. ALT 2003: Sapporo, Japan
- Ricard Gavaldà, Klaus P. Jantke, Eiji Takimoto:
Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings. Lecture Notes in Computer Science 2842, Springer 2003, ISBN 3-540-20291-9
Invited Papers
- Thomas Eiter:
Abduction and the Dualization Problem. 1-2 - Genshiro Kitagawa:
Signal Extraction and Knowledge Discovery Based on Statistical Modeling. 3-14 - Akihiko Takano:
Association Computation for Information Access. 15 - Naftali Tishby:
Efficient Data Representations That Preserve Information. 16 - Thomas Zeugmann:
Can Learning in the Limit Be Done Efficiently? 17-38
Regular Contributions
Inductive Inference
- Sandra Zilles:
Intrinsic Complexity of Uniform Learning. 39-53 - Eric Martin, Arun Sharma, Frank Stephan:
On Ordinal VC-Dimension and Some Notions of Complexity. 54-68 - Jin Uemura, Masako Sato:
Learning of Erasing Primitive Formal Systems from Positive Examples. 69-83 - Frank J. Balbach:
Changing the Inference Type - Keeping the Hypothesis Space. 84-98
Learning and Information Extraction
- Jan Arpe, Rüdiger Reischuk:
Robust Inference of Relevant Attributes. 99-113 - Yusuke Suzuki, Takayoshi Shoudai, Satoshi Matsumoto, Tomoyuki Uchida, Tetsuhiro Miyahara:
Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables. 114-128
Learning with Queries
- Steffen Lange, Sandra Zilles:
On the Learnability of Erasing Pattern Languages in the Query Model. 129-143 - Satoshi Matsumoto, Yusuke Suzuki, Takayoshi Shoudai, Tetsuhiro Miyahara, Tomoyuki Uchida:
Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries. 144-158
Learning with Non-linear Optimization
- Jingdong Wang, Jianguo Lee, Changshui Zhang:
Kernel Trick Embedded Gaussian Mixture Model. 159-174 - Tijl De Bie, Michinari Momma, Nello Cristianini:
Efficiently Learning the Metric with Side-Information. 175-189 - Shaojun Wang, Dale Schuurmans:
Learning Continuous Latent Variable Models with Bregman Divergences. 190-204 - Joel Ratsaby:
A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation. 205-220
Learning from Random Examples
- Jirí Síma:
On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays. 221-233 - John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann:
Learning a Subclass of Regular Patterns in Polynomial Time. 234-246 - Colin de la Higuera, José Oncina
:
Identification with Probability One of Stochastic Deterministic Linear Languages. 247-258
Online Prediction
- Ilia Nouretdinov, Vladimir Vovk:
Criterion of Calibration for Transductive Confidence Machine with Limited Feedback. 259-267 - Vladimir Vovk:
Well-Calibrated Predictions from Online Compression Models. 268-282 - Ilia Nouretdinov, Vladimir V. V'yugin, Alex Gammerman:
Transductive Confidence Machine Is Universal. 283-297 - Marcus Hutter:
On the Existence and Convergence of Computable Universal Priors. 298-312
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