14. ALT 2003: Sapporo, Japan

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, Alexander 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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