A Survey on Pattern Recognition Applications of Support Vector Machines

  title={A Survey on Pattern Recognition Applications of Support Vector Machines},
  author={Hyeran Byun and Seong-Whan Lee},
The SVM is a new type of pattern classifier based on a novel statistical learning technique that has been recently proposed by Vapnik and his co-workers. Unlike traditional methods such as neural networks, which minimize the empirical training error, SVMs aim at minimizing an upper bound of the generalization error through maximizing the margin between the separating hyperplane and the data. Since SVMs are known to generalize well even in high dimensional spaces under small training sample… CONTINUE READING
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