Support vector machines based on K-means clustering for real-time business intelligence systems

@article{Wang2005SupportVM,
  title={Support vector machines based on K-means clustering for real-time business intelligence systems},
  author={Jiaqi Wang and Xindong Wu and Chengqi Zhang},
  journal={IJBIDM},
  year={2005},
  volume={1},
  pages={54-64}
}
Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KMSVM… CONTINUE READING
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