10. ALT 1999: Tokyo, Japan

Osamu Watanabe, Takashi Yokomori (Eds.): Algorithmic Learning Theory, 10th International Conference, ALT '99, Tokyo, Japan, December 6-8, 1999, Proceedings. Lecture Notes in Computer Science 1720 Springer 1999, ISBN 3-540-66748-2

Invited Lectures

Katharina Morik:
Tailoring Representations to Different Requirements. 1-12
Robert E. Schapire:
Theoretical Views of Boosting and Applications. 13-25
Kenji Yamanishi:
Extended Stochastic Complexity and Minimax Relative Loss Analysis. 26-38

Regular Contributions

Neural Networks

Sumio Watanabe:
Algebraic Analysis for Singular Statistical Estimation. 39-50
Kenji Fukumizu:
Generalization Error of Limear Neural Networks in Unidentifiable Cases. 51-62
Jirí Wiedermann:
The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa. 63-76

Learning Dimension

José L. Balcázar, Jorge Castro, David Guijarro, Hans-Ulrich Simon:
The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract). 77-92
Andrew R. Mitchell, Tobias Scheffer, Arun Sharma, Frank Stephan:
The VC-Dimension of Subclasses of Pattern. 93-105
Theodoros Evgeniou, Massimiliano Pontil:
On the V_gamma Dimension for Regression in Reproducing Kernel Hilbert Spaces. 106-117

Inductive Inference

Steffen Lange, Gunter Grieser:
On the Strength of Incremental Learning. 118-131
Peter Rossmanith:
Learning from Random Text. 132-144
Phil Watson:
Inductive Learning with Corroboration. 145-156

Inductive Logic Programming

Kouichi Hirata:
Flattening and Implication. 157-168
Yutaka Sasaki:
Induction of Logic Programs Based on psi-Terms. 169-181
Richard Nock:
Complexity in the Case against Accuracy: When Building one Function-Free Horn Clause is as Hard as Any. 182-193
Nobuhiro Morita, Makoto Haraguchi, Yoshiaki Okubo:
A Method of Similarity-Driven Knowledge Revision for Type Specializations. 194-205

PAC Learning

Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz:
PAC Learning with Nasty Noise. 206-218
Francesco De Comité, François Denis, Rémi Gilleron, Fabien Letouzey:
Positive and Unlabeled Examples Help Learning. 219-230
Dennis Cheung:
Learning Real Polynomials with a Turing Machine. 231-240

Mathematical Tools for Learning

Carlos Domingo:
Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E³ Algorithm. 241-251
Ryan M. Rifkin, Massimiliano Pontil, Alessandro Verri:
A Note on Support Vector Machine Degeneracy. 252-263

Learning Recursive Functions

Jochen Nessel:
Learnability of Enumerable Classes of Recursive Functions from "Typical" Examples. 264-275
Frank Stephan, Thomas Zeugmann:
On the Uniform Learnability of Approximations to Non-Recursive Functions. 276-290

Query Learning

Montserrat Hermo, Víctor Lavín:
Learning Minimal Covers of Functional Dependencies with Queries. 291-300
Víctor Dalmau:
Boolean Formulas are Hard to Learn for most Gate Bases. 301-312
David Guijarro, Jun Tarui, Tatsuie Tsukiji:
Finding Relevant Variables in PAC Model with Membership Queries. 313-

On-Line Learning

Yuri Kalnishkan:
Genral Linear Relations among Different Types of Predictive Complexity. 323-334
Eiji Takimoto, Manfred K. Warmuth:
Predicting Nearly as well as the best Pruning of a Planar Decision Graph. 335-346
Sally A. Goldman, Stephen Kwek:
On Learning Unions of Pattern Languages and Tree Patterns. 347-363

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