LIBTwinSVM: A Library for Twin Support Vector Machines
Amir M. Mir, Mahdi Rahbar, Jalal A. Nasiri
TL;DR
LIBTwinSVM is an open-source library that offers efficient implementations of Twin Support Vector Machines, including tools for model selection, visualization, and user interfaces, optimized for large-scale classification tasks.
Contribution
This work introduces LIBTwinSVM, a comprehensive library with functionalities for fast TSVM estimation, visualization, and user interfaces, enhancing practical application and accessibility.
Findings
Effective for large-scale classification problems
Demonstrates high efficiency and accuracy
Provides extensive tools and interfaces
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.
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Taxonomy
TopicsFace and Expression Recognition · Machine Learning and Algorithms · Machine Learning in Bioinformatics