Learnability of Augmented Naive Bayes in Nonimal Domains
نویسندگان
چکیده
It is well-known that Naive Bayes can only represent linearly separable functions in binary domains. But the learnability of general Augmented Naive Bayes is open. Little work is done on the learnability of Bayesian networks in nominal domains, a general case of binary domains. This paper explores the learnability of Augmented Naive Bayes in nominal domains. We introduce a complexity measure for nominal functions, and prove upper bounds of the learnability of Augmented Naive Bayes in terms of that measure. Our results deepen our theoretical understanding of the learnability (and limitations) of Naive Bayes, Tree Augmented Naive Bayes, and general Augmented Naive Bayes with diier-ent levels of complexity.
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