Simplicity, Truth, and Clustering

Abstract

Machine learning is a scientific discipline that can be divided into two main branches: supervised machine learning and unsupervised machine learning. In this paper, we aim to show just how simplicity matters in unsupervised contexts. This is important because unsupervised machine learning algorithms have barely received any attention in philosophy. Yet, there is a direct link between simplicity and truth in unsupervised contexts that we do not find in their supervised counterparts. This has thus far evaded philosophical discussions on simplicity.

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References found in this work

Prediction versus accommodation and the risk of overfitting.Christopher Hitchcock & Elliott Sober - 2004 - British Journal for the Philosophy of Science 55 (1):1-34.
Simplicity.Alan Baker - 2008 - Stanford Encyclopedia of Philosophy.
Instrumentalism, parsimony, and the akaike framework.Elliott Sober - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S112-S123.
Instrumentalism, Parsimony, and the Akaike Framework.Elliott Sober - 2002 - Philosophy of Science 69 (S3):S112-S123.

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