Development and Evaluation of Cost-Sensitive Universum-SVM

  title={Development and Evaluation of Cost-Sensitive Universum-SVM},
  author={Sauptik Dhar and Vladimir Cherkassky},
  journal={IEEE Transactions on Cybernetics},
Many machine learning applications involve analysis of high-dimensional data, where the number of input features is larger than/comparable to the number of data samples. Standard classification methods may not be sufficient for such data, and this provides motivation for nonstandard learning settings. One such new learning methodology is called learning through contradiction or Universum-support vector machine (U-SVM). Recent studies have shown U-SVM to be quite effective for sparse high… CONTINUE READING


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