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Varieties of Justification in Machine Learning

Corfield, David (2010) Varieties of Justification in Machine Learning. Minds and Machines, 20 (2). pp. 291-301. ISSN 0924-6495. (doi:10.1007/s11023-010-9191-1) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:27989)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s11023-010-9191-1

Abstract

Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.

Item Type: Article
DOI/Identification number: 10.1007/s11023-010-9191-1
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
Divisions: Faculties > Humanities > School of European Culture and Languages
Depositing User: David Corfield
Date Deposited: 27 Jun 2011 14:13 UTC
Last Modified: 06 Feb 2020 04:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/27989 (The current URI for this page, for reference purposes)
Corfield, David: https://orcid.org/0000-0003-0432-3221
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