Multi-resolution support vector machine

The support vector machine (SVM) is a new learning methodology based on Vapnik-Chervonenkis (VC) theory (Vapnik, 1982, 1995). SVM has recently attracted growing research interest due to its ability to learn classification and regression tasks with high-dimensional data. The SVM formulation uses kernel representation. The existing algorithm leaves the choice of the kernel type and kernel parameters to the user. This paper describes an important extension to the SVM method: the multiresolution SVM (M-SVM) in which several kernels of different scales can be used simultaneously to approximate the target function. The proposed M-SVM approach enables `automatic´ selection of the `optimal´ kernel width. This usually results in better prediction accuracy of SVM models