Studies in the Area of Support Vector Machines Reported from Beijing Jiaotong Un iversity (Feature Recognition of Complex Systems Using Cumulative Residual Tsall is Signal Entropy and Grey Wolf Optimized Support Vector Machine)

Robotics & Machine Learning Daily News2024，Issue(Mar.18) ：6-7.DOI:10.1016/j.eswa.2023.122246

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Elsevier

Studies in the Area of Support Vector Machines Reported from Beijing Jiaotong Un iversity (Feature Recognition of Complex Systems Using Cumulative Residual Tsall is Signal Entropy and Grey Wolf Optimized Support Vector Machine)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Support Vector Machines. According to news reporting from Beijing, People’s Repu blic of China, by NewsRx journalists, research stated, “In this paper, the cumul ative residual Tsallis singular entropy (CRTSE) is introduced to measure the com plex characteristics of nonlinear signals. Firstly, we do singular value decompo sition on time series, which can reduce the interference of noise on information extraction, and the singular values represent the information characteristics o f the signal.”

Key words

Beijing/People’s Republic of China/Asi a/Emerging Technologies/Machine Learning/Support Vector Machines/Vector Mach ines/Beijing Jiaotong University

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出版年

2024

Robotics & Machine Learning Daily News

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