Fast Support Vector Classifier (A low complexity alternative to SVM)
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 (2024). 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 .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox >
- AI, Data Science, and Statistics > Deep Learning Toolbox > Image Data Workflows > Pattern Recognition and Classification >
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Version | Published | Release Notes | |
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1.1.0.0 | A faster implementation (compiled with MEX) is available here:
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1.0.0.0 |