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.
An extended abstract based on an earlier version has been submitted for presentation to the Cognitive Science Society (in accordance with their policy of being of “limited circulation”).
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Powers, D.M.W. (2013). A Computationally and Cognitively Plausible Model of Supervised and Unsupervised Learning. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2013. Lecture Notes in Computer Science(), vol 7888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38786-9_17
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DOI: https://doi.org/10.1007/978-3-642-38786-9_17
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