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 |
|---|---|
| Pages (from-to) | 157-161 |
| Number of pages | 5 |
| Journal | Pattern Recognition |
| Volume | 38 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2005 Jan 1 |
| Externally published | Yes |
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Keywords
- Computational efficiency
- Support vector machine
- Training method
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Design efficient support vector machine for fast classification. / Zhan, Yiqiang; Shen, Dinggang.
In: Pattern Recognition, Vol. 38, No. 1, 01.01.2005, p. 157-161.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Design efficient support vector machine for fast classification
AU - Zhan, Yiqiang
AU - Shen, Dinggang
PY - 2005/1/1
Y1 - 2005/1/1
N2 - 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.
AB - 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.
KW - Computational efficiency
KW - Support vector machine
KW - Training method
UR - http://www.scopus.com/inward/record.url?scp=4644347704&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=4644347704&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2004.06.001
DO - 10.1016/j.patcog.2004.06.001
M3 - Article
AN - SCOPUS:4644347704
VL - 38
SP - 157
EP - 161
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 1
ER -