A two stages sparse SVM training

The small number of support vectors is an important factor for SVM to fast deal with very large scale problems. This paper considers fitting each class of data with a plane by a new model, which captures separability information between classes and can be solved by fast core set methods. Then traini...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics 2014-06, Vol.5 (3), p.425-434
Main Authors: Li, Ziqiang, Zhou, Mingtian, Lin, Hao, Pu, Haibo
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Complex Systems
Computational Intelligence
Control
Efficiency
Engineering
Mechatronics
Methods
Optimization
Original Article
Pattern Recognition
Quadratic programming
Robotics
Sparsity
Support vector machines
Systems Biology
Very large scale
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Summary:The small number of support vectors is an important factor for SVM to fast deal with very large scale problems. This paper considers fitting each class of data with a plane by a new model, which captures separability information between classes and can be solved by fast core set methods. Then training on the core sets of the fitting-planes yields a very sparse SVM classifier. The computing complexity of the proposed algorithm is up bounded by O ( 1 / ε ) . Experimental results show that the new algorithm trains faster than both CVM and SVMperf averagely, and with comparable generalization performance.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-013-0181-5