Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
Fast Parzen Window density estimator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Simplifying mixture models through function approximation
IEEE Transactions on Neural Networks
The Knowledge Engineering Review
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This correspondence proposes a fast Parzen density estimation algorithm that would be especially useful in nonparametric discriminant analysis problems. By preclustering the data and applying a simple branch and bound procedure to the clusters, significant numbers of data samples that would contribute little to the density estimate can be excluded without detriment to actual evaluation via the kernel functions. This technique is especially helpful in the multivariant case, and does not require a uniform sampling grid. The proposed algorithm may also be used in conjunction with the data reduction technique of Fukunaga and Hayes (1989) to further reduce the computational load. Experimental results are presented to verify the effectiveness of this algorithm.