Support vector machines with fuzzy entropy for training with a large datasets

Support vector machines are currently the state-of the-art models for many classification problems but they suffer from the complexity of their training algorithm that is at least quadratic with respect to the number of examples. Hence, it is hard to try to solve real-life problems having more than a few hundreds of thousands examples with SVM. The present paper proposes a new method based on the fuzzy entropy that can be used to preprocess original training data and the SVM is trained on a small subset (boundary subset) of the whole dataset. Comparing to other similar methods, the merit of our method is that there are no parameters for determining the border of subset. Preliminary experimental results indicate the benefits of our approach.