article

Self-Universum support vector machine

Authors:

Yong ShiAuthors Info & Claims

Personal and Ubiquitous Computing, Volume 18, Issue 8

Pages 1813 - 1819

https://doi.org/10.1007/s00779-014-0797-9

Published: 01 December 2014 Publication History

Abstract

In this paper, for an improved twin support vector machine (TWSVM), we give it a theoretical explanation based on the concept of Universum and then name it Self-Universum support vector machine (SUSVM). For the binary classification problem, SUSVM takes the positive class and negative class as Universum separately to construct two classification problems with Universum; therefore, two nonparallel hyperplanes are derived. SUSVM has several improved advantages compared with TWSVMs. Furthermore, we improve SUSVM by formulating it as a pair of linear programming problems instead of quadratic programming problems (QPPs), which leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on several benchmark datasets.

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Cited By

Liu BLi LXiao YWang KHu JLiu JChen QHuang R(2023)An Efficient Transfer Learning Method with Auxiliary InformationACM Transactions on Knowledge Discovery from Data10.1145/361293018:1(1-23)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3612930
Xiao YFeng JLiu B(2021)A new transductive learning method with universum dataApplied Intelligence10.1007/s10489-020-02113-451:8(5571-5583)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s10489-020-02113-4
Xiao YWen JLiu B(2021)A new multi-task learning method with universum dataApplied Intelligence10.1007/s10489-020-01954-351:6(3421-3434)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01954-3
Show More Cited By

Index Terms

Self-Universum support vector machine
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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Published In

cover image Personal and Ubiquitous Computing

Personal and Ubiquitous Computing Volume 18, Issue 8

December 2014

258 pages

ISSN:1617-4909

Issue’s Table of Contents

Copyright © Copyright © 2014 Springer-Verlag London.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2014

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Cited By

Liu BLi LXiao YWang KHu JLiu JChen QHuang R(2023)An Efficient Transfer Learning Method with Auxiliary InformationACM Transactions on Knowledge Discovery from Data10.1145/361293018:1(1-23)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1145/3612930
Xiao YFeng JLiu B(2021)A new transductive learning method with universum dataApplied Intelligence10.1007/s10489-020-02113-451:8(5571-5583)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s10489-020-02113-4
Xiao YWen JLiu B(2021)A new multi-task learning method with universum dataApplied Intelligence10.1007/s10489-020-01954-351:6(3421-3434)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01954-3
Zhu CWang PMiao DZhou RBalinsky ATan ATarng W(2019)Rank-consistency-based multi-view learning with UniversumProceedings of the 1st International Conference on Advanced Information Science and System10.1145/3373477.3373700(1-6)Online publication date: 15-Nov-2019
https://dl.acm.org/doi/10.1145/3373477.3373700
Zhu C(2018)Improved multi-kernel classification machine with Nyström approximation technique and Universum dataNeurocomputing10.1016/j.neucom.2015.10.102175:PA(610-634)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2015.10.102
Zhu C(2017)Double-fold localized multiple matrix learning machine with UniversumPattern Analysis & Applications10.1007/s10044-016-0548-920:4(1091-1118)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s10044-016-0548-9

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