Local-to-Global Support Vector Machines (LGSVMs)
作者：
Highlights：
• Support Vector Machines (SVMs) are a popular kernel method for supervised learning.
• Complexity costs and memory needs become prohibitive as the number of samples grows.
• LGSVMs split the original problem into overlapping local SVMs classification tasks.
• The Partition of Unity (PU) scheme ensures the definition of a global classifier.
• As theoretically analyzed and shown, LGSVMs reduce the required execution time.
摘要
•Support Vector Machines (SVMs) are a popular kernel method for supervised learning.•Complexity costs and memory needs become prohibitive as the number of samples grows.•LGSVMs split the original problem into overlapping local SVMs classification tasks.•The Partition of Unity (PU) scheme ensures the definition of a global classifier.•As theoretically analyzed and shown, LGSVMs reduce the required execution time.
论文关键词：Local-to-global support vector machines,Partition of unity,Supervised classification,Kernel models
论文评审过程：Received 8 April 2021, Revised 3 July 2022, Accepted 21 July 2022, Available online 22 July 2022, Version of Record 9 August 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108920
