A computationally and cognitively plausible model of supervised and unsupervised learning

Authors:
David M. W. Powers
Affiliations:
CSEM Centre for Knowledge & Interaction Technology, Flinders University, Adelaide, South Australia, Australia,Beijing Municipal Lab for Multimedia & Intelligent Software, BJUT, Beijing, Ch ...
Venue:
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Year:
2013

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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.