Abstract
Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.
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Pauplin, O., Jiang, J. (2010). A Dynamic Bayesian Network Based Structural Learning towards Automated Handwritten Digit Recognition. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_15
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DOI: https://doi.org/10.1007/978-3-642-13769-3_15
Publisher Name: Springer, Berlin, Heidelberg
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