Implements a low complexity classifier based on LMS training in
a nonlinearly expanded feature space based on simple RBF units.
The centers of the units are support vectors selected from the
training sample using a simple search algorithm based on novelty
R. Dogaru, “A hardware oriented classifier with simple constructive
training based on support vectors”, in Proceedings of CSCS-16, the
16th Int’l Conference on Control Systems and Computer Science,
May 22 - 26, 2007, Bucharest, Vol.1, pp. 415-418.
Dogaru, R. ; Dogaru, I.,
"An efficient finite precision RBF-M neural network architecture using support vectors"
in Neural Network Applications in Electrical Engineering (NEUREL), 2010 10th Symposium on
Digital Object Identifier: 10.1109/NEUREL.2010.5644089
Publication Year: 2010 , Page(s): 127 - 130
Radu Dogaru (2020). Fast Support Vector Classifier (A low complexity alternative to SVM) (https://www.mathworks.com/matlabcentral/fileexchange/49695-fast-support-vector-classifier-a-low-complexity-alternative-to-svm), MATLAB Central File Exchange. Retrieved .
A faster implementation (compiled with MEX) is available here:
minor change of name