Associative memory with uncorrelated inputs
Neural Computation（1996）
摘要
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.
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关键词
unsupervised learning algorithm,principal component analysis,uncorrelated input,hybrid learning scheme,squares solution,supervised learning problem,supervised learning algorithm,unsupervised learning,correlation matrix memory,supervised learning,associative memory,correlation matrix
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