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Fast Support Vector Classifier (A low complexity alternative to SVM)

version 1.1.0.0 (363 KB) by Radu Dogaru
A low complexity alterantive to SVM for classification problems (single and multiple class)

3 Downloads

Updated 25 May 2016

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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
detection.
Relevant papers:
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

Cite As

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 .

Comments and Ratings (2)

thanks about this files,
can you say,How Run this program ,please?

Can you please say how to run this file?.

Updates

1.1.0.0

A faster implementation (compiled with MEX) is available here:
https://github.com/radu-dogaru/Fast-Support-Vector-Classifier

1.1.0.0

minor change of name

MATLAB Release Compatibility
Created with R2008b
Compatible with any release
Platform Compatibility
Windows macOS Linux