When feasible, learning is a very attractive alternative to explicit programming. This is
particularly true in areas where the problems do not lend themselves to systematic …

This chapter surveys approximation algorithms for hard geometric problems. The problems
we consider typically take inputs that are point sets or polytopes in two-or three-dimensional …

In this paper we demonstrate that finite linear combinations of compositions of a fixed,
univariate function and a set of affine functionals can uniformly approximate any continuous …

Storing and using specific instances improves the performance of several supervised
learning algorithms. These include algorithms that learn decision trees, classification rules …

The problem of identifying an unknown regular set from examples of its members and
nonmembers is addressed. It is assumed that the regular set is presented by a minimally …

This chapter reproduces the English translation by B. Seckler of the paper by Vapnik and
Chervonenkis in which they gave proofs for the innovative results they had obtained in a …

INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …

We consider the problem of using queries to learn an unknown concept. Several types of
queries are described and studied: membership, equivalence, subset, superset …

Valiant (1984) and others have studied the problem of learning various classes of Boolean
functions from examples. Here we discuss incremental learning of these functions. We …

The basic question addressed in this paper is: how can a learning algorithm cope with
incorrect training examples? Specifically, how can algorithms that produce an …