A computationally and cognitively plausible model of supervised and unsupervised learning
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Date
2013-01-01
Authors
Powers, David Martin
Journal Title
Journal ISSN
Volume Title
Publisher
Springer-Verlag
Rights
Copyright © 2013 Springer-Verlag Berlin Heidelberg
Rights Holder
Springer-Verlag Berlin Heidelberg
Abstract
The issue of chance correction has been discussed for many decades in the context of
statistics, psychology and machine learning, with multiple measures being shown to
have desirable properties, including various definitions of Kappa or Correlation, and
the psychologically validated ΔP measures. In this paper, we discuss the relationships
between these measures, showing that they form part of a single family of measures,
and that using an appropriate measure can positively impact learning.
Description
Author version made available in accordance with the publisher's policy. "The final publication is available at link.springer.com”
Keywords
Citation
Powers, D.M. (2013). A computationally and cognitively plausible model of supervised and unsupervised learning. In D Liu et. al (Ed.), Advances in Brain Inspired Cognitive Systems: Vol 7888, 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings (pp. 145-156) Berlin: Springer Berlin Heidelberg.