Zhao, Kino (2018) A statistical learning approach to a problem of induction. In: UNSPECIFIED.

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Abstract

At its strongest, Hume's problem of induction denies the existence of any well justified assumptionless inductive inference rule. At the weakest, it challenges our ability to articulate and apply good inductive inference rules. This paper examines an analysis that is closer to the latter camp. It reviews one answer to this problem drawn from the VC theorem in statistical learning theory and argues for its inadequacy. In particular, I show that it cannot be computed, in general, whether we are in a situation where the VC theorem can be applied for the purpose we want it to.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Zhao, Kino yutingz3@uci.edu
Keywords:	statistical learning theory, problem of induction, model theory
Subjects:	Specific Sciences > Mathematics > Logic General Issues > Confirmation/Induction General Issues > Formal Learning Theory
Depositing User:	Dr. Kino Zhao
Date Deposited:	05 Nov 2018 16:58
Last Modified:	05 Nov 2018 16:58
Item ID:	15256
Subjects:	Specific Sciences > Mathematics > Logic General Issues > Confirmation/Induction General Issues > Formal Learning Theory
Date:	November 2018
URI:	https://philsci-archive.pitt.edu/id/eprint/15256

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