title: A single-iteration threshold Hamming network.
abstract: 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" subnet of the Hamming network (known to be the essential factor determining the time complexity of the network's computation) may be altogether discarded. For m growing exponentially in n, the resulting threshold Hamming network correctly classifies the input pattern in a single iteration, with probability approaching 1.
authors.affiliation: Sch. of Math. Sci., Tel Aviv Univ.
authors.forename: I
authors.initials: I
authors.lastname: Meilijson
authors.forename: E
authors.initials: E
authors.lastname: Ruppin
authors.forename: M
authors.initials: M
authors.lastname: Sipper