首页|Findings from Chongqing University in Support Vector Machines Reported (Lineariz ed Alternating Direction Method of Multipliers for Elastic-net Support Vector Ma chines)

Findings from Chongqing University in Support Vector Machines Reported (Lineariz ed Alternating Direction Method of Multipliers for Elastic-net Support Vector Ma chines)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Support Vec tor Machines. According to news reporting out of Chongqing, People's Republic of China, by NewsRx editors, research stated, "In many high-dimensional datasets, the phenomenon that features are relevant often occurs. Elastic-net regularizati on is widely used in support vector machines (SVMs) because it can automatically perform feature selection and encourage highly correlated features to be select ed or removed together." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Scientific and Technological Research Program of Chongqing M unicipal Education Commission. Our news journalists obtained a quote from the research from Chongqing Universit y, "Recently, some effective algorithms have been proposed to solve the elastic- net SVMs with different convex loss functions, such as hinge, squared hinge, hub erized hinge, pinball and huberized pinball. In this paper, we develop a lineari zed alternating direction method of multipliers (LADMM) algorithm to solve above elastic-net SVMs. In addition, our algorithm can be applied to solve some new e lastic-net SVMs such as elastic-net least squares SVM. Compared with some existi ng algorithms, our algorithm has comparable or better performances in terms of c omputational cost and accuracy. Under mild conditions, we prove the convergence and derive convergence rate of our algorithm."

ChongqingPeople's Republic of ChinaA siaAlgorithmsEmerging TechnologiesMachine LearningSupport Vector Machine sVector MachinesChongqing University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Apr.1)