Learning Time of Linear Associative Memory

Toshiyuki TANAKA  Hideki KURIYAMA  Yoshiko OCHIAI  Masao TAKI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E80-A   No.6   pp.1150-1156
Publication Date: 1997/06/25
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
neural network,  learning,  associative memory,  learning time,  gradient descent,  

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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 memoty 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.