首页|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)
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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NETL
NSTL
Elsevier
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.”
BeijingPeople’s Republic of ChinaAsi aEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mach inesBeijing Jiaotong University
NETL
NSTL
Elsevier
