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Improving Rbf Networks Classification Performance By Using K-Harmonic Means
In this paper, a clustering algorithm named KHarmonic means (KHM) was employed in the training of Radial Basis Function Networks (RBFNs). KHM organized the data in clusters and determined the centres of the basis function. The popular clustering algorithms, namely K-means (KM) and Fuzzy c-means (FCM), are highly dependent on the initial identification of elements that represent the cluster well. In KHM, the problem can be avoided. This leads to improvement in the classification performance whendoi:10.5281/zenodo.1058165 fatcat:43x5umlyh5aofarf3d2hcsj6xy