Special Issue on

Algorithmic Learning Theory

for ALT '96 in

ALT 96 Logo

Theoretical Computer Science Vol. 241, No. 1-2, June 28, 2000.

The Special Issue on Algorithmic Learning Theory for ALT'96 has been edited by Arun Sharma.

Table of Contents

Arun Sharma.

pp. 1 - 2

Paul Vitányi.
A discipline of evolutionary programming,

pp. 3 - 23

Philip M. Long.
Improved bounds about on-line learning of smooth-functions of a single variable,

pp. 25 - 35

Eiji Takimoto, Yoshifumi Sakai and Akira Maruoka.
The learnability of exclusive-or expansions based on monotone DNF formulas ,

pp. 37 - 50

V. Arvind and N.V. Vinodchandran.
Exact learning via teaching assistants,

pp. 51 - 81

Atsuyoshi Nakamura.
Query learning of bounded-width OBDDs,

pp. 83 - 114

John Case, Sanjay Jain and Frank Stephan.
Vacillatory and BC learning on noisy data,

pp. 115 - 141

Sanjay Jain, Efim Kinber, Steffen Lange, Rolf Wiehagen and Thomas Zeugmann.
Learning languages and functions by erasing,

pp. 143 - 189

Takeshi Shinohara and Hiroki Arimura.
Inductive inference of unbounded unions of pattern languages from positive data,

pp. 191 - 209

M.R.K. Krishna Rao.
Some classes of prolog programs inferable from positive data,

pp. 211 - 234

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