Special issue on VC dimension

[Joe Sill]

Machine learning theorists may be interested in a recent issue
of the journal Discrete Applied Mathematics (Vol 86, Number 1, 
August 18, 1998). This special issue, edited by John Shawe-Taylor, is 
devoted entirely to the VC dimension.

Contents:

"Combinatorial variability of Vapnik-Chervonenkis classes with 
applications to sample compression schemes"
S. Ben-David and A. Litman

"A graph-theoretic generalization of the Sauer-Shelah lemma"
N. Cesa-Bianchi and D. Haussler

"Scale-sensitive dimensions and skeleton estimates for classification"
M. Horvath and G. Lugosi

"Vapnik-Chervonenkis dimension of recurrent neural networks"
P. Koiran and E.D. Sontag

"The degree of approximation of sets in euclidean space using sets
with bounded Vapnik-Chervonenkis dimension"
V. Maiorov and J. Ratsaby

"The capacity of monotonic functions"
J. Sill

"Fluctuation bounds for sock-sorting and other stochastic processes"
D. Steinsaltz