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        High performance associative memory models and weight dilution

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        Author
        Davey, N.
        Adams, R.G.
        Hunt, S.
        Attention
        2299/7048
        Abstract
        The consequences of diluting the weights of the standard Hopfield architecture associative memory model, trained using perceptron like learning rules, is examined. A proportion of the weights of the network are removed; this can be done in a symmetric and asymmetric way and both methods are investigated. This paper reports experimental investigations into the consequences of dilution in terms of: capacity, training times and size of basins of attraction. It is concluded that these networks maintain a reasonable performance at fairly high dilution rates.
        Publication date
        2001
        Published in
        Procs of Int Conf on Neural Information Processing
        Other links
        http://hdl.handle.net/2299/7048
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