Muntasir Sheikh

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We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE)(More)
We develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF) network classifiers for two-class problems. Our approach integrates several concepts in probabilistic modelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At each stage of the OFS procedure, one model term is selected(More)
The objective of this paper is to evolve simple and effective methods for the optimal power flow (OPF) problem in thermal units, which are capable of obtaining optimal power generations for a large-scale system. In optimization, GA has some well-known advantages and disadvantages. The classical (derivative based) method is applied as economic dispatch(More)
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