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Friedman, N., Geiger, D. and Goldszmidt, M. (1997) Bayesian Network Classifiers. Machine Learning, 29, 131-163.
http://dx.doi.org/10.1023/A:1007465528199

has been cited by the following article:

• TITLE: Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers

  AUTHORS: Andreas Lianos, Yanyan Yang

  KEYWORDS: Ensemble Classification, Diversity, Training Data

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.7 No.2, May 26, 2015

  ABSTRACT: Typical supervised classification techniques require training instances similar to the values that need to be classified. This research proposes a methodology that can utilize training instances found in a different format. The benefit of this approach is that it allows the use of traditional classification techniques, without the need to hand-tag training instances if the information exists in other data sources. The proposed approach is presented through a practical classification application. The evaluation results show that the approach is viable, and that the segmentation of classifiers can greatly improve accuracy.