Oriented principal component analysis for large margin classifiers

@article{Bermejo2001OrientedPC,
  title={Oriented principal component analysis for large margin classifiers},
  author={Sergio Bermejo and Joan Cabestany},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2001},
  volume={14 10},
  pages={1447-61}
}
Large margin classifiers (such as MLPs) are designed to assign training samples with high confidence (or margin) to one of the classes. Recent theoretical results of these systems show why the use of regularisation terms and feature extractor techniques can enhance their generalisation properties. Since the optimal subset of features selected depends on the classification problem, but also on the particular classifier with which they are used, global learning algorithms for large margin… CONTINUE READING
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