| K. K. Yiu, M. W. Mak, and C. K. Li. Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification: A Comparative Study. Neural Computing and Applications, 8:235--245, February 1999. |
....thresholds. PDBNNs were used to implement a hierarchical face recognition system in [3] with excellent results (97.75 recognition, 2. 25 false rejection, 0 misclassification and 0 false acceptance) The characteristics of PDBNNs decision boundaries have been investigated in our previous study [4], where the strengths of PDBNNs are highlighted by comparing the recognition accuracy and decision boundaries of PDBNNs against those of GMMs. We have also demonstrated in [4] that the thresholding mechanism of PDBNNs is very effective in detecting data not belonging to any known classes. In light ....
....and 0 false acceptance) The characteristics of PDBNNs decision boundaries have been investigated in our previous study [4] where the strengths of PDBNNs are highlighted by comparing the recognition accuracy and decision boundaries of PDBNNs against those of GMMs. We have also demonstrated in [4] that the thresholding mechanism of PDBNNs is very effective in detecting data not belonging to any known classes. In light of this finding, this paper applies PDBNNs to speaker verification in an attempt to improve the robustness of speaker verification systems against intruder attacks. 2. ....
K. K. Yiu, M. W. Mak, and C. K. Li. Gaussian mixture models and probabilistic decision-based neural networks for pattern classification: A comparative study. Neural Computing & Applications, 8:235--245, 1999.
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K. K. Yiu, M. W. Mak, and C. K. Li. Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification: A Comparative Study. Neural Computing and Applications, 8:235--245, February 1999.
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