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The problem of determining the appropriate number of components is important in finite mixture modeling for pattern classification. This paper considers the application of an unsupervised clustering method called AutoClass to training of orthogonal Gaussian mixture models (OGMM). Actually, the number of components in OGMM of each class is selected based on AutoClass. In this way, the structures of...
Learning is important for classifiers. This paper proposes a new approach to handwritten digit recognition based on the max-min posterior pseudo-probabilities framework for learning pattern classification. Each digit class is modeled as a posterior pseudo-probability function, the parameters in which are trained from positive and negative samples of this digit class using the max-min posterior pseudo-probabilities...
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