ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Kovalchuk A. V., Bellyustin N. S. Classification algorithm of streaming signals based on the online support vector machine. Izvestiya VUZ. Applied Nonlinear Dynamics, 2015, vol. 23, iss. 5, pp. 62-79. DOI: 10.18500/0869-6632-2015-23-5-62-79

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Language: 
Russian
Heading: 
Applied Problems of Nonlinear Oscillation and Wave Theory
Article type: 
Article
UDC: 
519.6
DOI: 
10.18500/0869-6632-2015-23-5-62-79

Classification algorithm of streaming signals based on the online support vector machine

Autors: 
Kovalchuk Andrej Viktorovich, Institute of Applied Physics of the Russian Academy of Sciences
Bellyustin Nikolaj Sergeevich, Federal state budgetary scientific institution "Scientific-research radiophysical Institute"
Abstract: 

The work proposed a modification of support vector machines (SVM) to train and classify in real time (online) streams of data. The algorithm is tested on the data handwriting figures and shown that its error is comparable to SVM direct solution error. Speed and support vectors number of proposed SVM algorithm is smaller than in other known SVM implementations. Finally, a ternary classificator for 2-class problem is proposed which shows better results than binary.

Key words: 
Support vector machine
streaming signal classification
online learning
ternary classifier
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Received: 
19.11.2015
Accepted: 
19.11.2015
Published: 
29.04.2016
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