Single-Iteration Threshold Hamming Networks
نویسندگان
چکیده
We analyze in detail the performance of a Hamming network classifying inputs that are distorted versions of one of its m stored memory patterns. The activation function of the memory neurons in the original Hamming network is replaced by a simple threshold function. The resulting Threshold Hamming Network (THN) correctly classifies the input pattern, with probability approaching 1, using only O(mln m) connections, in a single iteration. The THN drastically reduces the time and space complexity of Hamming Network classifiers.
منابع مشابه
A single-iteration threshold Hamming network
We analyze in detail the performance of a Hamming network classifying inputs that are distorted versions of one of its m stored memory patterns, each being a binary vector of length n. It is shown that the activation function of the memory neurons in the original Hamming network may be replaced by a simple threshold function. By judiciously determining the threshold value, the "winner-take-all"...
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