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Erschienen in:
01.03.2008 | BIC-TA 2006
A new learning schema based on support vector for multi-classification
Erschienen in: Neural Computing and Applications | Ausgabe 2/2008
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Abstract
A novel learning schema SVCMR based on support vector is proposed in this paper to address M-class classification issue. It creates a tree-shaped decision frame where M/2 nodes are constructed with the three-separation model as the basic classifier. A class selection rule is defined to ensure basic classifiers be trained in turn on pair of classes with maximum feature distance. Class contours are extracted as data representatives to reduce training set size. Another point is that parameters involved in SVCMR are learned from data neighborhood, which brings adaptation to various datasets and avoids pricy cost spent on searching parameter spaces. Experiments on real datasets demonstrate the performance of SVCMR can be competitive to those state-of-the-art classifiers but with the higher effectiveness than them.