Algorithmic Learning Theory

1. Front Matter

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2. Grammatical inference: An old and new paradigm

   • Yasubumi Sakakibara

   Pages 1-24

3. Efficient learning of real time one-counter automata

   • Amr F. Fahmy, Robert S. Roos

   Pages 25-40

4. Learning strongly deterministic even linear languages from positive examples

   • Takeshi Koshiba, Erkki Mäkinen, Yuji Takada

   Pages 41-54

5. Language learning from membership queries and characteristic examples

   • Hiroshi Sakamoto

   Pages 55-65

6. Learning unions of tree patterns using queries

   • Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara

   Pages 66-79

7. Inductive constraint logic

   • Luc De Raedt, Wim Van Laer

   Pages 80-94

8. Incremental learning of logic programs

   • M. R. K. Krishna Rao

   Pages 95-109

9. Learning orthogonal F-Horn formulas

   • Akira Miyashiro, Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka

   Pages 110-122

10. Learning nested differences in the presence of malicious noise

    • Peter Auer

    Pages 123-137

11. Learning sparse linear combinations of basis functions over a finite domain

    • Atsuyoshi Nakamura, Shinji Miura

    Pages 138-150

12. Inferring a DNA sequence from erroneous copies (abstract)

    • John Kececioglu, Ming Li, John Tromp

    Pages 151-152

13. Machine induction without revolutionary paradigm shifts

    • John Case, Sanjay Jain, Arun Sharma

    Pages 153-168

14. Probabilistic language learning under monotonicity constraints

    • Léa Meyer

    Pages 169-184

15. Noisy inference and oracles

    • Frank Stephan

    Pages 185-200

16. Simulating teams with many conjectures

    • Bala Kalyanasundaram, Mahendran Velauthapillai

    Pages 201-214

17. Complexity of network training for classes of Neural Networks

    • Charles C. Pinter

    Pages 215-227

18. Learning ordered binary decision diagrams

    • Ricard Gavaldà, David Guijarro

    Pages 228-238

19. Simple PAC learning of simple decision lists

    • Jorge Castro, José L. Balcázar

    Pages 239-248

20. The complexity of learning minor closed graph classes

    • Carlos Domingo, John Shawe-Taylor

    Pages 249-260

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in October 1995.
The book contains 21 revised full papers selected from 46 submissions together with three invited contributions. It covers all current areas related to algorithmic learning theory, in particular the theory of machine learning, design and analysis of learning algorithms, computational logic aspects, inductive inference, learning via queries, artificial and biologicial neural network learning, pattern recognition, learning by analogy, statistical learning, inductive logic programming, robot learning, and gene analysis.

• algorithmic learning theory
• algorithms
• learning
• learning theory
• logic
• machine learning
• programming
• robot

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