Learning Time of Linear Associative Memory

概要

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Neural networks can be used as associative memories which can learn problems of acquiring input-output relations presented by examples. The learning time problem addresses how long it takes for a neural network to learn a given problem by a learning algorithm. As a solvable model to this problem we analyze the learning dynamics of the linear associative memory with the least-mean-square algorithm. Our result shows that the learning time τ of the linear associative memory diverges in τ ∝ (1-ρ)^<-2> as the memory rate ρ approaches 1. It also shows that the learning time exhibits the exponential dependence on ρ when ρ is small.
社団法人電子情報通信学会の論文
1997-06-25