Abstract
In this paper the support vector machine committee is proposed. For a practical pattern recognition problem, usually numerous of features can be used to represent the pattern. SVM committee can utilize these features efficiently and a classifier with better generalization can be obtained. Moreover, a novel aggregation approach of support vector machine committee is also proposed in this paper. The simulating results demonstrate the effectiveness and efficiency of our approach. © Springer-Verlag 2004.
Cite
CITATION STYLE
Sun, B. Y., Huang, D. S., Guo, L., & Zhao, Z. Q. (2004). Support vector machine committee for classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3173, 648–653. https://doi.org/10.1007/978-3-540-28647-9_106
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