Special Issue on

Algorithmic Learning Theory

for ALT 2002 in

Theoretical Computer Science

ALT 02 Logo

appeared as:

Theoretical Computer Science, Volume 350, Issue 1, January 2006.

The Special Issue on Algorithmic Learning Theory (ALT 2002) has been edited by Nicolò Cesa-Bianchi, Rüdiger Reischuk and Thomas Zeugmann.

Table of Contents

Nicolò Cesa-Bianchi, Rüdiger Reischuk and Thomas Zeugmann.

pp. 1-2

Kazuyuki Amano and Akira Maruoka,
On learning monotone Boolean functions under the uniform distribution,

pp. 3-12

Rocco A. Servedio,
On learning embedded midbit functions,
pp. 13-23

Nader H. Bshouty and Lynn Burroughs,
Maximizing agreements and coagnostic learning,
pp. 24-39

Jürgen Forster and Hans Ulrich Simon,
On the smallest possible dimension and the largest possible margin of linear arrangements representing given concept classes ,
pp. 40-48

Johannes Köbler, and Wolfgang Lindner,
The complexity of learning concept classes with polynomial general dimension,
pp. 49-62

Yusuke Suzuki, Takayoshi Shoudai, Tomoyuki Uchida and Tetsuhiro Miyahara,
Ordered term tree languages which are polynomial time inductively inferable from positive data,
pp. 63-90

Daniel Reidenbach
A non-learnable class of E-pattern languages,
pp. 91-102

Éric Martin, Arun Sharma and Frank Stephan,
Unifying logic, topology and learning in Parametric logic,
pp. 103-124

Susumu Hayashi
Mathematics based on incremental learning—Excluded middle and inductive inference
pp. 125-139

Bertram Fronhöfer and Akihiro Yamamoto
Hypothesis finding with proof theoretical appropriateness criteria,
pp. 140-162

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