Generalized Connectionist Associative Memory
Abstract
This paper presents a generalized associative memory model, which stores a collection of tu-ples whose components are sets rather than scalars. It is shown that all library patterns are stored stably. On the other hand spuri-ous memories may develop. Applications of this model to storage and retrieval of naturally-arising generalized sequences in bioinformatics are presented. The model is shown to work well for detection of novel generalized sequences against a large database of stored sequences, and for removal of noisy black pixels in a probe image against a very large set of stored images. 1
Cite
Text
Duffy and Jagota. "Generalized Connectionist Associative Memory." International Joint Conference on Artificial Intelligence, 1999.Markdown
[Duffy and Jagota. "Generalized Connectionist Associative Memory." International Joint Conference on Artificial Intelligence, 1999.](https://mlanthology.org/ijcai/1999/duffy1999ijcai-generalized/)BibTeX
@inproceedings{duffy1999ijcai-generalized,
title = {{Generalized Connectionist Associative Memory}},
author = {Duffy, Nigel P. and Jagota, Arun K.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1999},
pages = {833-839},
url = {https://mlanthology.org/ijcai/1999/duffy1999ijcai-generalized/}
}