LIBTwinSVM: A Library for Twin Support Vector Machines

2020/01/27 by Amir M. Mir, Mir, Amir M., Mahdi Rahbar +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning in Bioinformatics #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.2001.10073

arxiv created 2020/01/27 · openalex publication_date 2020/01/27 · arxiv updated 2020/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs). Our library provides a set of useful functionalities such as fast TSVMs estimators, model selection, visualization, a graphical user interface (GUI) application, and a Python application programming interface (API). The benchmarks results indicate the effectiveness of the LIBTwinSVM library for large-scale classification problems. The source code of LIBTwinSVM library, installation guide, documentation, and usage examples are available at https://github.com/mir-am/LIBTwinSVM.

Citations

• Matplotlib: A 2D Graphics Environment
• Twin Support Vector Machines for Pattern Classification

Related

• An enhanced KNN-based twin support vector machine with stable learning rules
• A Fast and Robust TSVM for Pattern Classification
• Multi-Task Learning Based on Support Vector Machines and Twin Support Vector Machines: A Comprehensive Survey
• Multi-view learning with privileged weighted twin support vector machine
• Granular Ball K-Class Twin Support Vector Classifier