Journal
NEUROCOMPUTING
Volume 74, Issue 17, Pages 3609-3618Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2011.06.026
Keywords
Decision support systems; Duality; Optimization methodology; Pattern classification; Support vector machine (SVM)
Categories
Funding
- National Natural Science Foundation of China [61075005]
- Fundamental Research Funds for the Central Universities
- PASCAL2 Network of Excellence
Ask authors/readers for more resources
Support vector machines (SVMs) are theoretically well-justified machine learning techniques, which have also been successfully applied to many real-world domains. The use of optimization methodologies plays a central role in finding solutions of SVMs. This paper reviews representative and state-of-the-art techniques for optimizing the training of SVMs, especially SVMs for classification. The objective of this paper is to provide readers an overview of the basic elements and recent advances for training SVMs and enable them to develop and implement new optimization strategies for SVM-related research at their disposal. (C) 2011 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available