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In this paper, a novel distance-based density estimation method is proposed, which considers the overall density function in the goodness-of-fit. In detail, the parameters of Gaussian mixture densities are estimated from samples, based on the distance of the cumulative distributions over the entire state space. Due to the ambiguous definition of the standard multivariate cumulative distribution, the...
Estimation of Distribution Algorithm (EDA) is a kind of new evolutionary algorithm which updates and samples from probabilistic model in evolutionary computation. Recently it is used to solve multi-objective problems. The key is how to construct probability model suitable for real distribution and how to keep diversity of solutions. In this paper a new multi-objective evolutionary of distribution...
Mean shift spectral clustering (MSSC) brings us an alternative for image segmentation. However, owing to being based on the classical Parzen window estimator (PW) and employing the full data sample for density estimation, the usefulness of MSSC is weakened. In this paper, the improved mean shift spectral clustering (IMSSC) algorithm is proposed by replacing PW with the reduced set density estimator...
Estimation of distribution algorithm (EDA) is a kind of evolutionary algorithm which updates and samples from probabilistic model in evolutionary course. The key of EDA is the construction of probability model suitable for real distribution. Gaussian distribution is widely used in EDAs but the assumption of normality is not realistic for many real-life problems. In this paper, a new EDA using kernel...
In this paper, a solution to empirical dependency measure is proposed. The main idea is to use the notion of predictability as a basis for dependency definition. Considering any nonlinear regression function between two random variables, the power of regression residuals or noise variance defines the desired dependency measure. The residuals variance can be directly computed by estimators without...
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