Special Issue on

Algorithmic Learning Theory

for ALT '97 in

ALT 97 Logo

Theoretical Computer Science Volume 261, Issue 1, 17 June 2001.

The Special Issue on Algorithmic Learning Theory for ALT '97 has been edited by Ming Li.


Table of Contents

Ming Li.
Foreword,


pp. 1
Sanjay Jain, Steffen Lange and Jochen Nessel.
On the learnability of recursively enumerable languages from good examples,
Abstract.
pp. 3 - 29
John Case, Sanjay Jain and Arun Sharma.
Synthesizing noise-tolerant language learners,
Abstract.
pp. 31 - 56
V. Vovk.
Probability theory for the Brier game,
Abstract.
pp. 57 - 79
Leonid Gurvits
A note on a scale-sensitive dimension of linear bounded functionals in Banach spaces,
Abstract.
pp. 81 - 90
Andris Ambainis, Kalvis Apsitis, Rusins Freivalds and Carl H. Smith.
Hierarchies of probabilistic and team FIN-learning,
Abstract.
pp. 91 - 117
Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger and Thomas Zeugmann.
Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries,
Abstract.
pp. 119 - 156
Wolfgang Maass.
On the relevance of time in neural computation and learning,
Abstract.
pp. 157 - 178
Eiji Takimoto, Akira Maruoka and Volodya Vovk.
Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme,
Abstract. 		pp. 179 - 209
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