Designing RBF classifiers for weighted boosting

The recent interest in combining neural networks has produced a variety of techniques. This paper deals with boosting methods, in particular, real AdaBoost schemes built up with radial basis function networks. Real Adaboost emphasis function can be divided into two different terms, the first only focus on the quadratic error of each pattern and the second only takes into account its "proximity" to the boundary. Incorporating to this fixed emphasis function an additional degree of freedom, that allows us to weight these two terms, and also to select the radial basis functions centroids according to the emphasized regions, we show performance improvements: an error rate reduction, a faster convergence, and overfitting robustness.