… Rather than simplifying the density, we restrict the nature of the decision boundary used by
… induced by the Bayes optimal classifier, we select a hypothesis from a restricted class. The …

… Bayesian classifier’s probability estimates are only optimal under quadratic loss if the
independence assumption holds, the classifier itself can be optimal … In spite of this restrictive view …

… We can think of the Naive Bayesian algorithm as being at the most restrictive end of the
feature dependence spectrum, in that it strictly allows no dependencies between features given …

… Unlike the naive Bayesian classifier, however, our classifier allows … While the induction of
the naive Bayesian classifier requires … To address this problem, we examine a restricted form of …

… the performance of the approximate Bayesian network to a semi-naïve Bayes classifer [15]. …
-naïve Bayes classifier if all the α’s are set to zero. We trained this semi-naïve Bayes classifier …

… ) are one type of large margin classifier, and the relationship between SVMs and the Bayes
rule was not clear. In this … For the remainder of the proof we restrict attention to this event. …

… the naive Bayes classifier in order to perform classification that is restricted to be independent
with respect to a given sensitive attribute. Such independency restrictions occur naturally …

… For visualization purposes, we restricted the plot for values within [-3, 3]. (c) When restricting
… for homoscedastic Gaussian distributions, this should be the classifier of our choice in the …

… We say that classifier is optimal on a given problem if its risk coincides with the Bayes risk.
… Our attention will be restricted to binary classification problems where the class is either 0 or …

… However, Bayesian network classifiers (BNCs) learned in the … in learning restricted BNCs
and non-BN classifiers. Using … significantly outperform the naive Bayesian classifier (NBC), tree …