A computationally and cognitively plausible model of supervised and unsupervised learning
Neural and Evolutionary Computing
2020-10-29 v1 Artificial Intelligence
Machine 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.
Cite
@article{arxiv.2010.14618,
title = {A computationally and cognitively plausible model of supervised and unsupervised learning},
author = {David M W Powers},
journal= {arXiv preprint arXiv:2010.14618},
year = {2020}
}
Comments
12 pages, 2 figures, 24 references. Amended version of paper presented at BICS 2013