Algorithmic Learning Theory: 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings (Lecture Notes in Computer Science)

This book constitutes the refereed proceedings of the 14th International Conference on Algorithmic Learning Theory, ALT 2003, held in Sapporo, Japan in October 2003. The 19 revised full papers presented together with 2 invited papers and abstracts of 3 invited talks were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on inductive inference, learning and information extraction, learning with queries, learning with non-linear optimization, learning from random examples, and online prediction. Front Matter....Pages - Abduction and the Dualization Problem....Pages 1-2 Signal Extraction and Knowledge Discovery Based on Statistical Modeling....Pages 3-14 Association Computation for Information Access....Pages 15-15 Efficient Data Representations That Preserve Information....Pages 16-16 Can Learning in the Limit Be Done Efficiently?....Pages 17-38 Intrinsic Complexity of Uniform Learning....Pages 39-53 On Ordinal VC-Dimension and Some Notions of Complexity....Pages 54-68 Learning of Erasing Primitive Formal Systems from Positive Examples....Pages 69-83 Changing the Inference Type – Keeping the Hypothesis Space....Pages 84-98 Robust Inference of Relevant Attributes....Pages 99-113 Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables....Pages 114-128 On the Learnability of Erasing Pattern Languages in the Query Model....Pages 129-143 Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries....Pages 144-158 Kernel Trick Embedded Gaussian Mixture Model....Pages 159-174 Efficiently Learning the Metric with Side-Information....Pages 175-189 Learning Continuous Latent Variable Models with Bregman Divergences....Pages 190-204 A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation....Pages 205-220 On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays....Pages 221-233 Learning a Subclass of Regular Patterns in Polynomial Time....Pages 234-246 Identification with Probability One of Stochastic Deterministic Linear Languages....Pages 247-258 Criterion of Calibration for Transductive Confidence Machine with Limited Feedback....Pages 259-267 Well-Calibrated Predictions from Online Compression Models....Pages 268-282 Transductive Confidence Machine Is Universal....Pages 283-297 On the Existence and Convergence of Computable Universal Priors....Pages 298-312 Back Matter....Pages -

This book constitutes the refereed proceedings of the 14th International Conference on Algorithmic Learning Theory, ALT 2003, held in Sapporo, Japan in October 2003.

The 19 revised full papers presented together with 2 invited papers and abstracts of 3 invited talks were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on inductive inference, learning and information extraction, learning with queries, learning with non-linear optimization, learning from random examples, and online prediction.

This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. Abduction is a fundamental mode of reasoning which was extensively studied by C.S. Peirce, who also introduced the term for inference of explanations for observed phenomena.