Learning the Structure of Augmented Bayesian Classifiers
نویسندگان
چکیده
منابع مشابه
A Theoretical and Experimental Evaluation of Augmented Bayesian Classifiers
Naive Bayes is a simple Bayesian network classifier with strong independence assumptions among features. This classifier despite its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of naive Bayes may improve the performance of the resulting structure. Augmented Bayesian Classifiers relax the independence assumptions of ...
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Machine learning has focused a lot of attention at Bayesian classifiers in recent years. It has seen that even Naive Bayes classifier performs well in many cases, it may be improved by introducing some dependency relationships among variables (Augmented Naive Bayes). Naive Bayes is incremental in nature but, up to now, there are no incremental algorithms for learning Augmented classifiers. When...
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Machine learning has focused a lot of attention at Bayesian classifiers in recent years. It has seen that even Naive Bayes classifier performs well in many cases, it may be improved by introducing some dependency relationships among variables (Augmented Naive Bayes). Naive Bayes is incremental in nature but, up to now, there are no incremental algorithms for learning Augmented classifiers. When...
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ورودعنوان ژورنال:
- International Journal on Artificial Intelligence Tools
دوره 11 شماره
صفحات -
تاریخ انتشار 2002