Weighted Linear Loss Large Margin Distribution Machine for Pattern Classification

扫码查看

原文链接

NETL
NSTL
万方数据

外文摘要：Compared with support vector machine,large margin distribution machine(LDM)has better general-ization performance.The central idea of LDM is to maximize the margin mean and minimize the margin variance simultaneously.But the computational complexity of LDM is high.In order to reduce the computational complexity of LDM,a weighted linear loss LDM(WLLDM)is proposed.The framework of WLLDM is built based on LDM and the weighted linear loss.The weighted linear loss is adopted instead of the hinge loss in WLLDM.This modification can transform the quadratic programming problem into a simple linear equation,resulting in lower computational complexity.Thus,WLLDM has the potential to deal with large-scale datasets.The WLLDM is similar in principle to the LDM algorithm,which can optimize the margin distribution and achieve better generalization performance.The WLLDM algorithm is compared with other models by conducting experiments on different datasets.The experimental results show that the proposed WLLDM has better generalization performance and faster training speed.

外文关键词：

Support vector machineLarge margin distribution machineWeighted linear lossPattern classifi-cation

作者：

Ling LIU、Maoxiang CHU、Rongfen GONG、Liming LIU、Yonghui YANG

展开 >

作者单位：

School of Electronic and Information Engineering,University of Science and Technology Liaoning,Anshan 114051,China

基金：

National Natural Science Foundation of ChinaNatural Science Foundation of Liaoning ProvinceBasic Scientific Research Project of Education Department of Liaoning Province

项目编号：

717711122022-MS-353LJKMZ20220640

出版年：

2024

DOI：

10.23919/cje.2022.00.156

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(3)