Probability density estimation based on SVM

The problem of probability density estimation can be used in many areas in signal processing, such as regression and classification. In this paper, a density estimation approach based on support vector machine (SVM) was developed. Our algorithm has robust results and sparse solutions compared with Parzen´s method. Besides, we used fundamental splines instead of Gaussian kernels in order to further reduce the computation. The simulations show that SVM method for density estimation has a moderately good performance and high convergence speed. Further more, a Bayesian classifier is constructed using the density estimation algorithm.