Mahalanobis distance-based ARTMAP network

A new ARTMAP-based network is proposed, which is in part a generalization of Williamson´s Gaussian ARTMAP. The training of the new network is based on activation and match functions that are equal and identical to Mahalanobis distance. The classification treats the clusters obtained through training as Gaussian mixture models. The training process has improved its efficiency due to the fact that the repeated searches for the resonant node have been eliminated. In addition, the inverse covariance matrices are computed recurrently. The new network is analyzed and compared with the fuzzy ARTMAP and Gaussian ARTMAP. The results from the new network have shown much better hit rates at fewer output nodes on several benchmark problems. A complexity analysis of the three networks is also provided.