Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications
Statistics and Computing
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Statistical pattern recognition is one of the most studied and applied approaches in the area of pattern recognition. Mixture modelling of densities is an efficient statistical pattern recognition method for continuous data. We propose a classifier based on the beta mixture models for strictly bounded and asymmetrically distributed data. Due to the property of the mixture modelling, the statistical dependence in a multi-dimensional variable is captured, even with the conditional independence assumption in each mixture component. A synthetic example and the USPS handwriting digit data was used to verify the effectiveness of this approach. Compared to the conventional Gaussian mixture models (GMM), the beta mixture models has a better performance on data which has strictly bounded value and asymmetric distribution. The performance of beta mixture models is about equivalent to that of GMM applied to data transformed via a strictly increasing link function.