Abstract
In this paper, generalized statistical independence is proposed from the viewpoint of generalized multiplication characterized by a monotonically increasing function and its inverse function, and it is implemented in naive Bayes models. This paper also proposes an idea of their estimation method which directly uses empirical marginal distributions to retain simplicity of calculation. Our method is interpreted as an optimization of a rough approximation of the Bregman divergence so that it is expected to have a kind of robust property. Effectiveness of our proposed models is shown by numerical experiments on some benchmark data sets.
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Fujimoto, Y., Murata, N. (2010). A Generalization of Independence in Naive Bayes Model. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_19
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DOI: https://doi.org/10.1007/978-3-642-15381-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15380-8
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