✦ LIBER ✦

On the Learnability of Rich Function Classes

✍ Scribed by Joel Ratsaby; Vitaly Maiorov

Publisher: Elsevier Science
Year: 1999
Tongue: English
Weight: 202 KB
Volume: 58
Category: Article
ISSN: 0022-0000
DOI: 10.1006/jcss.1998.1604

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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