Maximum Margin Structure Learning of Bayesian Network Classifiers

TitleMaximum Margin Structure Learning of Bayesian Network Classifiers
Publication TypeConference Paper
Year of Publication2011
AuthorsPernkopf, F., M. Wohlmayr, and M. Mücke
Conference Name36th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)
Pages2076-2079
Abstract

Recently, the margin criterion has been successfully used for parameter optimization in graphical models. We introduce maximum margin based structure learning for Bayesian network classifiers and demonstrate its advantages in terms of classification performance compared to traditionally used discriminative structure learning methods. In particular, we provide empirical results for generative structure learning and two discriminative structure learning approaches on handwritten digit recognition tasks. We show that maximum margin structure learning outperforms other structure learning methods. Furthermore, we present classification results achieved with different bitwidth for representing the parameters of the classifiers.

Citation Keyciteulike:9002352
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