Single-Iteration Threshold Hamming Networks
Abstract
We analyze in detail the performance of a Hamming network clas(cid:173) sifying 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) cor(cid:173) rectly 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 Net(cid:173) work classifiers.
Cite
Text
Meilijson et al. "Single-Iteration Threshold Hamming Networks." Neural Information Processing Systems, 1992.Markdown
[Meilijson et al. "Single-Iteration Threshold Hamming Networks." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/meilijson1992neurips-singleiteration/)BibTeX
@inproceedings{meilijson1992neurips-singleiteration,
title = {{Single-Iteration Threshold Hamming Networks}},
author = {Meilijson, Isaac and Ruppin, Eytan and Sipper, Moshe},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {564-571},
url = {https://mlanthology.org/neurips/1992/meilijson1992neurips-singleiteration/}
}