Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
Recognition of degraded characters using dynamic Bayesian networks
Pattern Recognition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Multistream Articulatory Feature-Based Models for Visual Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using a local discovery ant algorithm for Bayesian network structure learning
IEEE Transactions on Evolutionary Computation
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Semantic analysis of soccer video using dynamic Bayesian network
IEEE Transactions on Multimedia
Automatic Meeting Segmentation Using Dynamic Bayesian Networks
IEEE Transactions on Multimedia
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Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. The classification performance greatly relies on the choice of a DBN model that will best describe the dependencies in each class of data. In this paper, we present DBN models trained for the classification of handwritten digit. Two approaches to improve the suitability of the models are presented. One uses a fixed DBN structure, and is based on an Evolutionary Algorithm optimisation of the selection and of the layout of the observations for each class of data. The second approach is about learning part of the structure of the models from the training set of each class. Parameter learning is then performed for each DBN. Classification results are presented for the described models, and compared with previously published results. Both approaches were found to improve the recognition rate compared to previous results.