Abstract
This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM). First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set, Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation.
Original language | English |
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Pages (from-to) | 157-161 |
Number of pages | 5 |
Journal | Pattern Recognition |
Volume | 38 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2005 Jan |
Externally published | Yes |
Keywords
- Computational efficiency
- Support vector machine
- Training method
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence