In many applications, an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification learning. In this paper, we analyzed the role of unlabeled data in the context of naive Bayesian learning. Experimental results show that including unlabeled data as part of training data can significantly improve the performance of classification accuracy. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lee, C. H. (2006). A semi-naive Bayesian learning method for utilizing unlabeled data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4251 LNAI-I, pp. 187–194). Springer Verlag. https://doi.org/10.1007/11892960_23
Mendeley helps you to discover research relevant for your work.