Special Issue on

Algorithmic Learning Theory

for ALT 2003 in

Theoretical Computer Science

ALT 03 Logo

appeared as:

Theoretical Computer Science, Volume 364, Issue 1, November 2006.

The Special Issue on Algorithmic Learning Theory for ALT 2003 has been edited by Richard Gavaldà and Eiji Takimoto

Table of Contents

Richard Gavaldà and Eiji Takimoto,

pp. 1-2

Ilia Nouretdinov and Vladimir Vovk,
Criterion of calibration for transductive confidence machine with limited feedback

pp. 3-9

Vladimir Vovk,
Well-calibrated predictions from on-line compression models
pp. 10-26

Marcus Hutter,
On generalized computable universal priors and their convergence
pp. 27-41

Sandra Zilles,
An approach to intrinsic complexity of uniform learning
pp. 42-61

Eric Martin, Arun Sharma, and Frank Stephan,
On ordinal VC-dimension and some notions of complexity
pp. 62-76

Thomas Zeugmann,
From learning in the limit to stochastic finite learning
pp. 77-97

Jin Uemura and Masako Sato,
Learning of erasing primitive formal systems from positive examples
pp. 98-114

John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, and Thomas Zeugmann,
Learning a subclass of regular patterns in polynomial time
pp. 115-131

Genshiro Kitagawa
Signal extraction and knowledge discovery based on statistical modeling
pp. 132-142

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