An effective support vector machines (SVMs) performance using hierarchical clustering

@article{Awad2004AnES,
  title={An effective support vector machines (SVMs) performance using hierarchical clustering},
  author={Mamoun A. Awad and Latifur Khan and Farokh B. Bastani and I-Ling Yen},
  journal={16th IEEE International Conference on Tools with Artificial Intelligence},
  year={2004},
  pages={663-667}
}
The training time for SVMs to compute the maximal marginal hyper-plane is at least O(N/sup 2/) with the data set size N, which makes it nonfavorable for large data sets. This work presents a study for enhancing the training time of SVMs, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the dynamically growing self-organizing tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering… CONTINUE READING

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