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Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

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第一作者： Shangping,Zhong

第一单位： College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350108, China. Electronic address: spzhong@fzu.edu.cn.

作者： Shangping,Zhong ^[1] ; Tianshun,Chen ^[2] ; Fengying,He ^[2] ; Yuzhen,Niu ^[2]

作者单位： College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350108, China. Electronic address: spzhong@fzu.edu.cn. ^[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350108, China. ^[2]

关键词 Difference of convex functions Difference of increasing functions Fast Gaussian kernel learning method Hoffman’s outer approximation method Kernel target alignment criterion Specially structured global optimization

医学主题词算法(Algorithms);人工智能(Artificial Intelligence);分类法(Classification);正态分布(Normal Distribution);软件(Software)

DOI 10.1016/j.neunet.2014.05.014

PMID 24929345

发布时间 2018-12-02

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