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An empirical study of the naive bayes classifier
, 2001
"... The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence is generally a poor assumption, in practice naive Bayes often competes well with more sophisticated classifiers. Our broad goal is to understand the data characteristics ..."
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The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence is generally a poor assumption, in practice naive Bayes often competes well with more sophisticated classifiers. Our broad goal is to understand the data characteristics
Generalized Naive Bayes Classifiers
, 2005
"... This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory and pred ..."
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Cited by 5 (0 self)
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This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory
The indifferent naive bayes classifier.
, 2003
"... Abstract The Naive Bayes classifier is a simple and accurate classifier. This paper shows that assuming the Naive Bayes classifier model and applying Bayesian model averaging and the principle of indifference, an equally simple, more accurate and theoretically well founded classifier can be obtaine ..."
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Abstract The Naive Bayes classifier is a simple and accurate classifier. This paper shows that assuming the Naive Bayes classifier model and applying Bayesian model averaging and the principle of indifference, an equally simple, more accurate and theoretically well founded classifier can
The Naive Bayes Classifier
, 2014
"... Classification as a goal I Machine learning focuses on identifying classes (classification), while social science is typically interested in locating things on latent traits (scaling) I But the two methods overlap and can be adapted – will demonstrate later using the Naive Bayes classifier I Applyin ..."
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Classification as a goal I Machine learning focuses on identifying classes (classification), while social science is typically interested in locating things on latent traits (scaling) I But the two methods overlap and can be adapted – will demonstrate later using the Naive Bayes classifier I
Evolving Extended Naive Bayes Classifier
 Cheung: Proc. Sixth IEEE International Conference on Data Mining. IEEE, Los Alamitos (2006
"... Naïve Bayes classifiers are a very simple tool for classification problems, although they are based on independence assumptions that do not hold in most cases. Extended naïve Bayes classifiers also rely on independence assumption, but break them down to artificial subclasses, in this way becoming mo ..."
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Naïve Bayes classifiers are a very simple tool for classification problems, although they are based on independence assumptions that do not hold in most cases. Extended naïve Bayes classifiers also rely on independence assumption, but break them down to artificial subclasses, in this way becoming
Hierarchical Mixtures of Naive Bayes Classifiers
, 2002
"... Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this paper we study combining multiple naive Bayes classifiers by using the hierarchical mixture ..."
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Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this paper we study combining multiple naive Bayes classifiers by using the hierarchical
Pairwise Naive Bayes classifier
 PROCEEDINGS OF THE LWA 2006, LERNEN WISSENSENTDECKUNG ADAPTIVITÄT
, 2006
"... Class binarizations are effective methods for improving weak learners by decomposing multiclass problems into several twoclass problems. This paper analyzes how these methods can be applied to a Naive Bayes learner. The key result is that the pairwise variant of Naive Bayes is equivalent to a reg ..."
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regular Naive Bayes. This result holds for several aggregation techniques for combining the predictions of the individual classifiers, including the commonly used voting and weighted voting techniques. On the other hand, Naive Bayes with oneagainstall binarization is not equivalent to a regular Naive
Naïve Bayes Classifiers for User Modeling
 Proceedings of the Conference on User Modeling
, 1999
"... In this paper we discuss how machine learning, and specifically how naive Bayes classifiers, can be used for user modeling tasks. We argue that in general, machine learning techniques should be used to improve a user modeling system’s interactions with users. We further argue that a naive Bayes clas ..."
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Cited by 7 (0 self)
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In this paper we discuss how machine learning, and specifically how naive Bayes classifiers, can be used for user modeling tasks. We argue that in general, machine learning techniques should be used to improve a user modeling system’s interactions with users. We further argue that a naive Bayes
GAB: Graph Augmented Bayes Classifier
"... This paper proposes a new classification approach; we call the Graph Augmented Bayes classifier (GAB). We show that naive Bayes classifier is a special case of GAB under the conditional independence assumption. GAB relaxes the conditional independence assumptions and takes into account of the influe ..."
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This paper proposes a new classification approach; we call the Graph Augmented Bayes classifier (GAB). We show that naive Bayes classifier is a special case of GAB under the conditional independence assumption. GAB relaxes the conditional independence assumptions and takes into account
BAYES CLASSIFIER IN MULTIDIMENSIONAL DATA CLASSIFICATION
"... Abstract: This paper deals with the classification of objects into the limited number of classes. Objects are characterised by nfeatures, e.g. ndimensional vectors describe them. The paper focuses on the Bayes classifier based on the probability principle, with the fixed number of the features dur ..."
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Abstract: This paper deals with the classification of objects into the limited number of classes. Objects are characterised by nfeatures, e.g. ndimensional vectors describe them. The paper focuses on the Bayes classifier based on the probability principle, with the fixed number of the features
Results 1  10
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