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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)

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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 (2021). 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)

Alireza Shafiee

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

Vasumathi Ganesh

Can you please say how to run this file?.

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

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