A computationally and cognitively plausible model of supervised and unsupervised learning

October 11, 2020 ยท Declared Dead ยท ๐Ÿ› International Conference on Advances in Brain Inspired Cognitive Systems

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Authors David M W Powers arXiv ID 2010.14618 Category cs.NE: Neural & Evolutionary Cross-listed cs.AI, stat.ML Citations 9 Venue International Conference on Advances in Brain Inspired Cognitive Systems Last Checked 4 months ago
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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