Semantic Scholar uses AI to extract papers important to this topic.
In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to… Expand A basic assumption in traditional machine learning is that the training and test data distributions should be identical. This… Expand Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and… Expand Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its… Expand Naive Bayes is often used as a baseline in text classification because it is fast and easy to implement. Its severe assumptions… Expand We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a… Expand The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence… Expand The naive Bayesclassifiergreatly simplify learning byassumingthatfeaturesareindependent given class. Although independenceis… Expand The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in… Expand Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the… Expand