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.
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Sun, BY., Huang, DS., Guo, L., Zhao, ZQ. (2004). Support Vector Machine Committee for Classification. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_106
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DOI: https://doi.org/10.1007/978-3-540-28647-9_106
Publisher Name: Springer, Berlin, Heidelberg
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