Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Attractor memory with self-organizing input
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
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In hybrid learning schemes a layer of unsupervised learning is followed by supervised learning. In this situation a connection between two unsupervised learning algorithms, principal component analysis and decorrelation, and a supervised learning algorithm, associative memory, is shown. When associative memory is preceded by principal component analysis or decorrelation it is possible to take advantage of the lack of correlation among inputs to associative memory to show that correlation matrix memory is a least squares solution to the supervised learning problem.