Discrete Applied Mathematics - Special issue: Vapnik-Chervonenkis dimension
A Compression Approach to Support Vector Model Selection
The Journal of Machine Learning Research
Measure Theory and Probability Theory (Springer Texts in Statistics)
Measure Theory and Probability Theory (Springer Texts in Statistics)
Unlabeled Compression Schemes for Maximum Classes
The Journal of Machine Learning Research
A geometric approach to sample compression
The Journal of Machine Learning Research
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Set systems of finite VC dimension are frequently used in applications relating to machine learning theory and statistics. Two simple types of VC classes which have been widely studied are the maximum classes (those which are extremal with respect to Sauer's lemma) and so-called Dudley classes, which arise as sets of positivity for linearly parameterized functions. These two types of VC class were related by Floyd, who gave sufficient conditions for when a Dudley class is maximum. It is widely known that Floyd's condition applies to positive Euclidean half-spaces and certain other classes, such as sets of positivity for univariate polynomials. In this paper we show that Floyd's lemma applies to a wider class of linearly parameterized functions than has been formally recognized to date. In particular we show that, modulo some minor technicalities, the sets of positivity for any linear combination of real analytic functions is maximum on points in general position. This includes sets of positivity for multivariate polynomials as a special case.