English

PAC learnability of a concept class under non-atomic measures: a problem by Vidyasagar

Machine Learning 2010-11-08 v1

Abstract

In response to a 1997 problem of M. Vidyasagar, we state a necessary and sufficient condition for distribution-free PAC learnability of a concept class C\mathscr C under the family of all non-atomic (diffuse) measures on the domain Ω\Omega. Clearly, finiteness of the classical Vapnik-Chervonenkis dimension of C\mathscr C is a sufficient, but no longer necessary, condition. Besides, learnability of C\mathscr C under non-atomic measures does not imply the uniform Glivenko-Cantelli property with regard to non-atomic measures. Our learnability criterion is stated in terms of a combinatorial parameter \VC(Cmodω1)\VC({\mathscr C}\,{\mathrm{mod}}\,\omega_1) which we call the VC dimension of C\mathscr C modulo countable sets. The new parameter is obtained by ``thickening up'' single points in the definition of VC dimension to uncountable ``clusters''. Equivalently, \VC(C\moddω1)d\VC(\mathscr C\modd\omega_1)\leq d if and only if every countable subclass of C\mathscr C has VC dimension d\leq d outside a countable subset of Ω\Omega. The new parameter can be also expressed as the classical VC dimension of C\mathscr C calculated on a suitable subset of a compactification of Ω\Omega. We do not make any measurability assumptions on C\mathscr C, assuming instead the validity of Martin's Axiom (MA).

Cite

@article{arxiv.1006.5090,
  title  = {PAC learnability of a concept class under non-atomic measures: a problem by Vidyasagar},
  author = {Vladimir Pestov},
  journal= {arXiv preprint arXiv:1006.5090},
  year   = {2010}
}

Comments

14 pages, 1 figure, latex 2e with Springer macros

R2 v1 2026-06-21T15:41:16.434Z