Efficient Discriminative Learning of Mixture of Bayesian Networks for Sequence Classification
نویسندگان
چکیده
Recently, it has been shown that Bayesian Network Classifier (BNC), a generative model applied to a classification task, yields comparable performance to sophisticated discriminative methods such as SVMs and C4.5 [1]. Improved classification performance of BNCs is due, in part, to optimization of the conditional likelihood (CML) [2]. Unfortunately, the CML optimization, often implemented as a gradient search, is computationally demanding, which is especially prohibitive for sequence domains with complex models such as Hidden Markov Models.
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