|Title||Maximum Margin Structure Learning of Bayesian Network Classifiers|
|Publication Type||Conference Paper|
|Year of Publication||2011|
|Authors||Pernkopf, F., M. Wohlmayr, and M. Mücke|
|Conference Name||36th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)|
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.