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Scaling SVM by Hierarchical Micro-Clustering

  • SVM is not scalable to the number of data objects in terms of training time and memory usage
  • H. Yu, J. Yang, and J. Han, “Classifying Large Data Sets Using SVM with Hierarchical Clusters”, KDD'03)
  • CB-SVM (Clustering-Based SVM)
    • Given limited amount of system resources (e.g., memory), maximize the SVM performance in terms of accuracy and the training speed
    • Use micro-clustering to effectively reduce the number of points to be considered
    • At deriving support vectors, de-cluster micro-clusters near “candidate vector” to ensure high classification accuracy

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