Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery
Abstract
A combined neural network and rule-based approach is suggested as a general framework for pattern recognition. This approach enables unsu(cid:173) pervised and supervised learning, respectively, while providing probability estimates for the output classes. The probability maps are utilized for higher level analysis such as a feedback for smoothing over the output la(cid:173) bel maps and the identification of unknown patterns (pattern "discovery"). The suggested approach is presented and demonstrated in the texture - analysis task. A correct classification rate in the 90 percentile is achieved for both unstructured and structured natural texture mosaics. The advan(cid:173) tages of the probabilistic approach to pattern analysis are demonstrated.
Cite
Text
Greenspan et al. "Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery." Neural Information Processing Systems, 1991.Markdown
[Greenspan et al. "Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/greenspan1991neurips-combined/)BibTeX
@inproceedings{greenspan1991neurips-combined,
title = {{Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery}},
author = {Greenspan, Hayit K. and Goodman, Rodney and Chellappa, Rama},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {444-451},
url = {https://mlanthology.org/neurips/1991/greenspan1991neurips-combined/}
}