Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers

The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis of learning problems in the PAC framework. For polynomial learnability, we seek upper bounds on the V-C dimension that are polynomial in the syntactic complexity of concepts. Such upper bounds are automatic for discrete concept classes, but hitherto little has been known about what general conditions guarantee polynomial bounds on V-C dimension for classes in which concepts and examples are represented by tuples of real numbers. In this paper, we show that for two general kinds of concept class the V-C dimension is polynomially bounded in the number of real numbers used to define a problem instance. One is classes where the criterion for membership of an instance in a concept can be expressed as a formula (in the first-order theory of the reals) with fixed quantification depth and exponentially-bounded length, whose atomic predicates are polynomial inequalities of exponentially-bounded degree...

• Type: Conference
• Year: 1993
• Where: COLT
• Authors: Paul W. Goldberg, Mark Jerrum
• Keywords: COLT 1993, Machine Learning, Polynomial, Polynomial Bounds, V-C Dimension