The probably approximately correct (PAC) model of learning and its extension to real-valued function classes sets a rigorous framework based upon which the complexity of learning a target from a function class using a finite sample can be computed. There is one main restriction, however, that the function class have a finite VC-dimension or scale-sensitive pseudo-dimension. In this paper we present an extension of the PAC framework with which rich function classes with possibly infinite pseudo-dimension may be learned with a finite number of examples and a finite amount of partial information. As an example we consider learning a family of infinite dimensional Sobolev classes. © 1999 Academic Press.
CITATION STYLE
Ratsaby, J., & Maiorov, V. (1999). On the learnability of rich function classes. Journal of Computer and System Sciences, 58(1), 183–192. https://doi.org/10.1006/jcss.1998.1604
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