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. © 2013 Springer-Verlag.
CITATION STYLE
Powers, D. M. W. (2013). A computationally and cognitively plausible model of supervised and unsupervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7888 LNAI, pp. 145–156). https://doi.org/10.1007/978-3-642-38786-9_17
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