A Multi-Class Classification Weighted Least Squares Twin Support Vector Hypersphere Using Local Density Information

A Multi-Class Classification Weighted Least Squares Twin Support Vector Hypersphere Using Local Density Information

To overcome the disadvantages of the least squares twin support vector hypersphere (LS-TSVH), some improvements are proposed in this paper. First, LS-TSVH ignores the local sample information; it treats each sample equally when constructing the separating hyperspheres, which causes LS-TSVH to be hig...

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Bibliographic Details
Main Authors: Qing Ai, Anna Wang, Aihua Zhang, Yang Wang, Haijing Sun
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Least squares twin support vector hypersphere
local density information
multi-class classification
Newton downhill algorithm
Online Access:https://ieeexplore.ieee.org/document/8315446/

Internet

https://ieeexplore.ieee.org/document/8315446/

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