@inproceedings{19f9f9f6ba9e4679ab0f1bf50d98d529,
title = "Comparison of several learning subspace methods for classification",
abstract = "Several competition-based methods for classification are compared. Special attention is paid to subspace methods which are based on computing the projections of the patterns on the principal component vectors of the correlation matrices that span the pattern subspaces. A decision learning rule which updates the correlation matrices can be used to adjust the class boundary and improve the performance of the classification. A learning subspace method is proposed, and some other classification methods are reviewed. In this comparison, all of the methods are applied to a texture classification problem and the performance results are presented.",
author = "Taur, {J. S.} and Kung, {S. Y.}",
year = "1991",
doi = "10.1109/icassp.1991.150540",
language = "English (US)",
isbn = "0780300033",
series = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
publisher = "Publ by IEEE",
pages = "1069--1072",
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",
}