@inproceedings{0e100baed42747f88f0fc87ee0c05a5a,
title = "Competition-based supervised learning algorithm for nonlinear discriminant functions",
abstract = "A basic competition-based model is the now-classic perceptron net using linear discriminant functions. The competition-based learning is extended to the general cases of nonlinear discriminant functions. Generalized perceptron learning rules for the binary-classification and multiple-classification cases are proposed. The convergency properties of the general perceptrons are established. Simulation results on texture classification applications are provided.",
author = "Kung, {S. Y.} and Mao, {W. D.}",
year = "1991",
doi = "10.1109/icassp.1991.150542",
language = "English (US)",
isbn = "0780300033",
series = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
publisher = "Publ by IEEE",
pages = "1073--1076",
booktitle = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
note = "Proceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 ; Conference date: 14-05-1991 Through 17-05-1991",
}