A computationally and cognitively plausible model of supervised and unsupervised learning
October 11, 2020 ยท Declared Dead ยท ๐ International Conference on Advances in Brain Inspired Cognitive Systems
"No code URL or promise found in abstract"
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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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