PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions
D. Corfield, B. Schölkopf and V. Vapnik
Journal for General Philosophy of Science Volume 40, Number 1, pp. 51-58, 2009.

Abstract

We compare Karl Popper’s ideas concerning the falsifiability of a theory with similar notions from the part of statistical learning theory known as VC-theory. Popper’s notion of the dimension of a theory is contrasted with the apparently very similar VC-dimension. Having located some divergences, we discuss how best to view Popper’s work from the perspective of statistical learning theory, either as a precursor or as aiming to capture a different learning activity.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6305
Deposited By:Bernhard Schölkopf
Deposited On:08 March 2010