首页|Study Results from Chongqing University Update Understanding of Support Vector M achines (Bounded Quantile Loss for Robust Support Vector Machines-based Classifi cation and Regression)
Study Results from Chongqing University Update Understanding of Support Vector M achines (Bounded Quantile Loss for Robust Support Vector Machines-based Classifi cation and Regression)
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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 Chongqing, People’s Re public of China, by NewsRx journalists, research stated, “In this paper, motivat e by quantile in the field statistics and bounded linex loss function a novel ro bust bounded quantile loss is proposed for improving the performance of support vector machine (SVM) and support vector regression (SVR). The bounded quantile l oss has some important properties such as asymmetry, non-convexity, which make S VM and SVR based on bounded quantile (BQ-SVM and BQ-SVR) robust to noise.”
ChongqingPeople’s Republic of ChinaA siaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Ma chinesChongqing University
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