Multi-scale kernel methods for classification

We propose the enhancement of Support Vector Machines for classification, by the use of multi-scale kernel structures (based on wavelet philosophy) which can be linearly combined in a spatially varying way. This provides a good tradeoff between ability to generalize well in areas of sparse training vectors and ability to fit fine detail of the decision surface in areas where the training vector density is sufficient to provide this information. Our algorithm is a sequential machine learning method in that progressively finer kernel functions are incorporated in successive stages of the learning process. Its key advantage is the ability to find the appropriate kernel scale for every local region of the input space.