Researchers’ Work from Northeastern University Focuses on Support Vector Machine s (A Distributionally Robust Chanceconstrained Kernel-free Quadratic Surface Su pport Vector Machine)
Researchers’ Work from Northeastern University Focuses on Support Vector Machine s (A Distributionally Robust Chanceconstrained Kernel-free Quadratic Surface Su pport Vector Machine)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Support Vector Machines is now available. According to news reporting from Liaoning, People’s Republic of China, by NewsRx journalists, research stated, “This paper studies the proble m of constructing a robust nonlinear classifier when the data set involves uncer tainty and only the first- and second -order moments are known a priori. A distr ibutionally robust chanceconstrained kernel -free quadratic surface support vect or machine (SVM) model is proposed using the moment information of the uncertain data.” Funders for this research include National Science Foundation (NSF), National Na tural Science Foundation of China (NSFC), Hainan Provincial Natural Science Foun dation of China, Foundation of Yunnan Key Laboratory of Service Computing Grant.
Key words
Liaoning/People’s Republic of China/As ia/Emerging Technologies/Machine Learning/Support Vector Machines/Vector Mac hines/Northeastern University
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