Special Issue on

Algorithmic Learning Theory

for ALT 2004 in

Theoretical Computer Science

ALT 04 Logo

appeared as:


Theoretical Computer Science, Volume 382, Issue 3, September 6, 2007.

The Special Issue on Algorithmic Learning Theory (ALT 2004) has been edited by Shai Ben-David, John Case and Thomas Zeugmann.


Table of Contents

Shai Ben-David, John Case and Thomas Zeugmann.
Foreword,
pp. 167-169
Eric Martin, Arun Sharma and Frank Stephan.
On the data consumption benefits of accepting increased uncertainty,
Abstract.
pp. 170-182
Jérôme Besombes and Jean-Yves Marion.
Learning tree languages from positive examples and membership queries,
Abstract.
pp. 183-197
Robert H. Sloan, Balázs Szörényi, and György Turán.
Revising threshold functions,
Abstract.
pp. 198-208
Andrei Bulatov, Hubie Chen, and Víctor Dalmau.
Learning intersection-closed classes with signatures,
Abstract.
pp. 209-220
Nicolò Cesa-Bianchi.
Applications of regularized least squares to pattern classification,
Abstract.
pp. 221-231
Amiran Ambroladze, Emilio Parrado-Hernández and John Shawe-Taylor.
Complexity of pattern classes and the Lipschitz property,
Abstract.
pp. 232-246
Marcus Hutter and Andrej Muchnik.
On semimeasures predicting Martin-Löf random sequences,
Abstract.
pp. 247-261
Hans Ulrich Simon,
On the complexity of working set selection,
Abstract.
pp. 262-279
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