首页|Data from Chongqing University Advance Knowledge in Support Vector Machines (Fused Robust Geometric Nonparallel Hyperplane Support Vector Machine for Pattern Classification)
Data from Chongqing University Advance Knowledge in Support Vector Machines (Fused Robust Geometric Nonparallel Hyperplane Support Vector Machine for Pattern Classification)
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NETL
NSTL
Elsevier
Research findings on Support Vector Machines are discussed in a new report. According to news reporting out of Chongqing, People’s Republic of China, by NewsRx editors, research stated, “Recently, introducing nonconvex loss functions in support vector machine (SVM) to improve the robustness against varies noises has been drawing much attention. In this paper, we first construct a new robust capped asymmetric elastic net (CaEN) loss function.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Chongqing University, “Second, we describe a novel robust Huberized kernel-based (HK) loss function and theoretically demonstrate several important properties, such as smoothness, boundness and the trade-off between the standard least squares and the truncated least squares. Finally, we apply the CaEN loss and the HK loss into elastic net nonparallel hyperplane SVM (ENNHSVM) to develop a fused robust geometric nonparallel SVM (FRGNHSVM). The proposed FRGNHSVM not only inherits the advantages of ENNHSVM but also improves the robustness of classification problems. An efficient Pegasos-based DC (difference of convex functions) algorithm is implemented to solve the FRGNHSVM optimization problem. In comparison with four famous SVMs, including Lagrangian SVM, twin SVM, pinball SVM and C-loss twin SVM, experimental results on simulations and twelve UCI datasets show that the proposed FRGNHSVM can often improve more than 5% average prediction accuracy.”
ChongqingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesChongqing University
NETL
NSTL
Elsevier
