Evolving multivariate mixture density estimates for classification

Donald E. Waagen; M. D. Parsons; John R. McDonnell; Jeffrey D. Argast

doi:10.1117/12.179226

30 June 1994 Evolving multivariate mixture density estimates for classification

Donald E. Waagen, M. D. Parsons, John R. McDonnell, Jeffrey D. Argast

Proceedings Volume 2304, Neural and Stochastic Methods in Image and Signal Processing III; (1994) https://doi.org/10.1117/12.179226
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Finite mixture models (mixture models) estimate probability density functions based on a weighted combination of density functions. This work investigates a combined stochastic and deterministic optimization approach of a generalized kernel function for multivariate mixture density estimation. Mixture models are selected and optimized by combining the optimization characteristics of a multi-agent stochastic optimization algorithm, based on evolutionary programming, and the EM algorithm. A classification problem is approached by optimizing a mixture density estimate for each class. Rissanen's minimum description length criterion provides the selection mechanism for evaluating mixture models. A comparison of each class' posterior probability (Bayes rule) provides the classification decision procedure. A 2-D, two- class classification problem is posed, and classification performance of the optimal mixture models is compared with a kernel estimator whose bandwidth is optimized using the technique of least-squares cross-validation.

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Donald E. Waagen, M. D. Parsons, John R. McDonnell, and Jeffrey D. Argast "Evolving multivariate mixture density estimates for classification", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); https://doi.org/10.1117/12.179226

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