Weighted Parzen windows for pattern classification

This paper introduces the weighted-Parzen-window classifier. The proposed technique uses a clustering procedure to find a set of reference vectors and weights which are used to approximate the Parzen-window (kernel-estimator) classifier. The weighted-Parzen-window classifier requires less computation and storage than the full Parzen-window classifier. Experimental results showed that significant savings could be achieved with only minimal, if any, error rate degradation for synthetic and real data sets.