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Hebbian Learning and Negative Feedback Networks


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The central idea of Hebbian Learning and Negative Feedback Networks is that neural networks use negative feedback activation can use simple Hebbian learning to regulate themselves so that they reveal an interesting structure in data sets. By changing the rules for network learning, it demonstrates how to perform Principal Component Analysis, exploration Projection Pursuit, Independent Component Analysis, Factor Analysis & various topology preserving mapping for such data sets.

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