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.
| Author(s): | Corfield, D. |
| Journal: | Minds and Machines |
| Volume: | 20 |
| Number (issue): | 2 |
| Pages: | 291-301 |
| Year: | 2010 |
| Month: | July |
| Day: | 0 |
| Bibtex Type: | Article (article) |
| DOI: | 10.1007/s11023-010-9191-1 |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@article{6853,
title = {Varieties of Justification in Machine Learning},
journal = {Minds and Machines},
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.},
volume = {20},
number = {2},
pages = {291-301},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jul,
year = {2010},
slug = {6853},
author = {Corfield, D.},
month_numeric = {7}
}