A Computationally and Cognitively Plausible Model of Supervised and Unsupervised Learning

David Powers

doi:10.1007/978-3-642-38786-9_17

A Computationally and Cognitively Plausible Model of Supervised and Unsupervised Learning

David Powers

Research output: Contribution to conference › Paper › peer-review

5 Citations (Scopus)

Abstract

Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.

Original language	English
Pages	145-156
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-38786-9_17
Publication status	Published - 23 Jul 2013
Event	International Conference on Brain Inspired Cognitive Systems - Duration: 9 Jul 2013 → …

Conference

Conference	International Conference on Brain Inspired Cognitive Systems
Period	9/07/13 → …

Keywords

AdaBoost
Chance-corrected evaluation
Kappa
Perceptron

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10.1007/978-3-642-38786-9_17

Cite this

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abstract = "Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.",

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AB - Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.

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KW - Chance-corrected evaluation

KW - Kappa

KW - Perceptron

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ER -

A Computationally and Cognitively Plausible Model of Supervised and Unsupervised Learning

Abstract

Conference

Keywords

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