The nature of statistical learning theory
The nature of statistical learning theory
Feature selection with dynamic mutual information
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Forward-Constrained Regression Algorithm for Sparse Kernel Density Estimation
IEEE Transactions on Neural Networks
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In this research, a local kernel regression method was proposed to improve the computational efficiency after analyzing the kernel weights of the nonparametric kernel regression Based on the correlation between the distribution function and the probability density function, together with the nonparametric local kernel regression we developed a new probability density estimation method With the proper setting of the sparse factor, the number of the kernels involved in the kernel smooth was controlled, and the density was estimated with highly fitness and smoothness According to the simulations, we can see that the proposed method shows a very well performance both in the accuracy and the efficiency.