Probability Density Estimation Using Advanced Support Vector Machines and the Expectation Maximization Algorithm
نویسندگان
چکیده
This paper presents a new approach for the probability density function estimation using the Support Vector Machines (SVM) and the Expectation Maximization (EM) algorithms. In the proposed approach, an advanced algorithm for the SVM density estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the parameters of the kernel function which is used by the SVM algorithm, the proposed approach uses the EM algorithm for an automatic optimization of the kernel. Experimental evaluation using simulated data set shows encouraging results.
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