首页|Researchers from South China Normal University Describe Findings in Support Vector Machines (Splitting Method for Support Vector Machine With Lower Semi-continuous Loss*)
Researchers from South China Normal University Describe Findings in Support Vector Machines (Splitting Method for Support Vector Machine With Lower Semi-continuous Loss*)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Support Vector Machines. According to news originating from Guangdong, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, we study the splitting method for support vector machine in reproducing kernel Hilbert space with lower semi-continuous loss function.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangdong Province. Our news journalists obtained a quote from the research from South China Normal University, “We equivalently transfer support vector machine in reproducing kernel Hilbert space with lower semi-continuous loss function to a finite-dimensional Optimization and propose the splitting method based on alternating direction method of multipliers. If the loss function is lower semi-continuous and subanalytic, we use the Kurdyka-Lojasiewicz property of the augmented Lagrangian function to show that the iterative sequence induced by this splitting method giobally converges to a stationary point.” According to the news editors, the research concluded: “The numerical experiments also demonstrate the effectiveness of the splitting method.” This research has been peer-reviewed.
GuangdongPeople’s Republic of ChinaAsiaEmerging Tech- nologiesMachine LearningSupport Vector MachinesVector MachinesSouth China Normal University
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