Abstract
The empirical risk minimization methods were often used to estimate the multifunctional sensor regression function in signal reconstruction. The small size of sample data would lead to the problem of poor generalization capability and overfitting. Support vector machine (SVM) is a novel machine learning method based on structural risk minimization, and it can improve generalization capability and restrain overfitting. In this paper, an optimal ν Support Vector Regression (ν-SVR) algorithms have been proposed for multifunctional sensor reconstruction, which combined ν-SVR with particle swarm optimization (PSO), achieving accurate estimation of both the hyperparameters and reconstruction function. The results of emulation and theory analysis indicate that the proposed algorithm is more accurate and reliable for signal reconstruction. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4204
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: TELKOMNIKA Indonesian Journal of Electrical Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
