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In this paper, we propose a corrective bandwidth learning algorithm for Kernel Density Estimation (KDE)-based classifiers. The objective of the corrective bandwidth learning algorithm is to minimize the expected error-rate. It utilizes a gradient descent technique to obtain the appropriate bandwidths. The proposed classifier is called the "Empirical Mixture Model" (EMM) classifier. Experiments were conducted on a set of multivariate multi-class classification problems with various data sizes. The proposed classifier has an error-rate closer to the true model compared to conventional KDE-based classifiers for both small and large data sizes. Additional experiments on standard machine learning datasets showed that the proposed bandwidth learning algorithm performed very well in gen-eral.