Learning Accurate and Understandable Rules from Svm Classifiers

@inproceedings{Chen2004LearningAA,
  title={Learning Accurate and Understandable Rules from Svm Classifiers},
  author={Fei Chen},
  year={2004}
}
Despite of their impressive classification accuracy in many high dimensional applications, Support Vector Machine (SVM) classifiers are hard to understand because the definition of the separating hyperplanes typically involves a large percentage of all features. In this paper, we address the problem of understanding SVM classifiers, which has not yet been well-studied. We formulate the problem as learning models to approximate trained SVMs that are more understandable while preserve most of the… CONTINUE READING