A computationally and cognitively plausible model of supervised and unsupervised learning

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.

12 pages, 2 figures, 24 references. Amended version of paper presented at BICS 2013