Algorithmic Learning Theory

1. Front Matter

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2. Managing complexity in neuroidal circuits

   • Leslie G. Valiant

   Pages 1-11

3. Learnability of exclusive-or expansion based on monotone DNF formulas

   • Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka

   Pages 12-25

4. Improved bounds about on-line learning of smooth functions of a single variable

   • Philip M. Long

   Pages 26-36

5. Query learning of bounded-width OBDDs

   • Atsuyoshi Nakamura

   Pages 37-50

6. Learning a representation for optimizable formulas

   • Hans Kleine Büning, Theodor Lettmann

   Pages 51-58

7. Limits of exact algorithms for inference of minimum size finite state machines

   • Arlindo L. Oliveira, Stephen Edwards

   Pages 59-66

8. Genetic fitness optimization using rapidly mixing Markov chains

   • Paul Vitányi

   Pages 67-82

9. The kindest cut: Minimum message length segmentation

   • Rohan A. Baxter, Jonathan J. Oliver

   Pages 83-90

10. Reducing complexity of decision trees with two variable tests

    • Pearson R. A., Smith E. K. T.

    Pages 91-99

11. The complexity of exactly learning algebraic concepts

    • V. Arvind, N. V. Vinodchandran

    Pages 100-112

12. Efficient learning of real time two-counter automata

    • Amr F. Fahmy, Robert S. Roos

    Pages 113-126

13. Cost-sensitive feature reduction applied to a hybrid genetic algorithm

    • Nada Lavrač, Dragan Gamberger, Peter Turney

    Pages 127-134

14. Effects of Feature Selection with ‘Blurring’ on neurofuzzy systems

    • Selwyn Piramuthu

    Pages 135-142

15. Boosting first-order learning

    • J. R. Quinlan

    Pages 143-155

16. Incorporating hypothetical knowledge into the process of inductive synthesis

    • Jānis Bārzdiņš, Ugis Sarkans

    Pages 156-168

17. Induction of Constraint Logic Programs

    • Lionel Martin, Christel Vrain

    Pages 169-176

18. Constructive learning of translations based on dictionaries

    • Noriko Sugimoto, Kouichi Hirata, Hiroki Ishizaka

    Pages 177-184

19. Inductive logic programming beyond logical implication

    • Jianguo Lu, Jun Arima

    Pages 185-198

20. Noise elimination in inductive concept learning: A case study in medical diagnosis

    • Dragan Gamberger, Nada Lavrač, Sašo Džeroski

    Pages 199-212

This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996.
The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas.

• Algorithmic Learning
• Algorithmisches Lernen
• Computational Learning
• Inductive Inference
• Induktive Inferenz
• Konzeptuelles Lernen
• Logic Programming
• Maschinelles Lernen
• algorithmic learning theory
• algorithms
• learning
• learning theory
• machine learning

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