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Discriminative simplification of mixture models
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Simplification of mixture models has recently emerged as an important issue in the field of statistical learning. The heavy computational demands of using large order models drove researches to investigate how to efficiently reduce the number of components in mixture models. The simplification, in solutions proposed so far, was performed by maximizing a certain measure of similarity to the original model, regardless of the discriminative qualities among models of different classes. This paper
doi:10.1109/icassp.2011.5946927
dblp:conf/icassp/Bar-YosefB11
fatcat:atw6rjm57zhgtd5smo6q3or4ee