Twin Support Vector Machine: A review from 2007 to 2014
| Autor: | Sonali Agarwal, Divya Tomar |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Structured support vector machine
business.industry Computer science Least Squares Twin Support Vector Machine Multiple Birth Support Vector Machine QA75.5-76.95 Management Science and Operations Research Machine learning computer.software_genre Class (biology) Computer Science Applications Relevance vector machine Support vector machine Bounded Twin Support Vector Machine Binary classification Electronic computers. Computer science Problem domain Twin Support Vector Machine Weighted least squares Twin Support Vector Machine Artificial intelligence Quadratic programming business Regression problems computer Information Systems |
| Zdroj: | Egyptian Informatics Journal, Vol 16, Iss 1, Pp 55-69 (2015) |
| ISSN: | 1110-8665 |
| DOI: | 10.1016/j.eij.2014.12.003 |
| Popis: | Twin Support Vector Machine (TWSVM) is an emerging machine learning method suitable for both classification and regression problems. It utilizes the concept of Generalized Eigen-values Proximal Support Vector Machine (GEPSVM) and finds two non-parallel planes for each class by solving a pair of Quadratic Programming Problems. It enhances the computational speed as compared to the traditional Support Vector Machine (SVM). TWSVM was initially constructed to solve binary classification problems; later researchers successfully extended it for multi-class problem domain. TWSVM always gives promising empirical results, due to which it has many attractive features which enhance its applicability. This paper presents the research development of TWSVM in recent years. This study is divided into two main broad categories - variant based and multi-class based TWSVM methods. The paper primarily discusses the basic concept of TWSVM and highlights its applications in recent years. A comparative analysis of various research contributions based on TWSVM is also presented. This is helpful for researchers to effectively utilize the TWSVM as an emergent research methodology and encourage them to work further in the performance enhancement of TWSVM. |
| Databáze: | OpenAIRE |
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