document: Mixture of experts classification using a hierarchical mixture model A three-level hierarchical mixture model for classification is presented that models the following data generation process : -LRB- 1 -RRB- the data are generated by a finite number of sources -LRB- clusters -RRB- , and -LRB- 2 -RRB- the generation mechanism of each source assumes the existence of individual internal class-labeled sources -LRB- subclusters of the external cluster -RRB- . The model estimates the posterior probability of class membership similar to a mixture of experts classifier . In order to learn the parameters of the model , we have developed a general training approach based on maximum likelihood that results in two efficient training algorithms . Compared to other classification mixture models , the proposed hierarchical model exhibits several advantages and provides improved classification performance as indicated by the experimental results
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