A Simple Additive Re-weighting Strategy for Improving Margins

We present a sample re-weighting scheme inspired by recent results in margin theory. The basic idea is to add to the training set replicas of samples which are not classified with a sufficient margin. We prove the convergence of the input distribution obtained in this way. As study case, we consider an instance of the scheme involving a 1-NN classifier implementing a Vector Quantization algorithm that accommodates tangent distance models. The tangent distance models created in this way have shown a significant improvement in generalization power with respect to the standard tangent models. Moreover, the obtained models were able to outperform state of the art algorithms, such as SVM.

• Type: Conference
• Year: 2001
• Where: IJCAI
• Authors: Fabio Aiolli, Alessandro Sperduti
• Keywords: IJCAI 2001, IJCAI 2007, Sample Re-weighting Scheme, Standard Tangent Models, Tangent Distance Models