A computationally and cognitively plausible model of supervised and unsupervised learning

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dc.contributor.authorPowers, David Martin
dc.date.accessioned2014-05-01T00:12:41Z
dc.date.available2014-05-01T00:12:41Z
dc.date.issued2013-01-01
dc.descriptionAuthor version made available in accordance with the publisher's policy. "The final publication is available at link.springer.com”en
dc.description.abstractThe 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.en
dc.identifier.citationPowers, 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.en
dc.identifier.doihttps://doi.org/10.1007/978-3-642-38786-9_17en
dc.identifier.isbn978-3-642-38785-2
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2328/27563
dc.language.isoen
dc.oaire.license.condition.licenseIn Copyright
dc.publisherSpringer-Verlagen
dc.relationhttp://purl.org/au-research/grants/ARC/DP0988686en
dc.relation.ispartofseriesLecture Notes in Computer Science;7888en
dc.rightsCopyright © 2013 Springer-Verlag Berlin Heidelbergen
dc.rights.holderSpringer-Verlag Berlin Heidelbergen
dc.titleA computationally and cognitively plausible model of supervised and unsupervised learningen
dc.typeArticleen
local.contributor.authorOrcidLookupPowers, David Martin: https://orcid.org/0000-0001-5998-2262en_US
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Australian Research Council (ARC)
David Powers
Flinders Open Access Research