Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
A Handwritten Numeral Character Classification Using Tolerant Rough Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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This paper introduces a hierarchical Gabor features(HGFs) and hierarchical bayesian network(HBN) for handwritten digit recognition. The HGFs represent a different level of information which is structured such that the higher the level, the more global information they represent, and the lower the level, the more localized information they represent. The HGFs are extracted by the Gabor filters selected using a discriminant measure. The HBN is a statistical model to represent a joint probability which encodes hierarchical dependencies among the HGFs. We simulated our method about a handwritten digit data set for recognition and compared it with the naive bayesian classifier, the backpropagation neural network and the k-nearest neighbor classifier. The efficiency of our proposed method was shown in that our method outperformed all other methods in the experiments.