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The indifferent naive bayes classifier
 Proceedings of the 16th International FLAIRS Conference
, 2003
"... 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 obtained. ..."
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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 obtained.
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
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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Cited by 183 (0 self)
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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
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
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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Cited by 2 (0 self)
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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
Incremental Discretization for NaïveBayes Classifier
"... Abstract. NaïveBayes classifiers (NB) support incremental learning. However, the lack of effective incremental discretization methods has been hindering NB’s incremental learning in face of quantitative data. This problem is further compounded by the fact that quantitative data are everywhere, from ..."
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Abstract. NaïveBayes classifiers (NB) support incremental learning. However, the lack of effective incremental discretization methods has been hindering NB’s incremental learning in face of quantitative data. This problem is further compounded by the fact that quantitative data are everywhere
Augmenting Naive Bayes Classifiers with Statistical Language Models
, 2003
"... We augment naive Bayes models with statistical ngram language models to address shortcomings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier ..."
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Cited by 65 (0 self)
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We augment naive Bayes models with statistical ngram language models to address shortcomings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier
Budgeted learning of naivebayes classifiers
 IN PROCEEDINGS OF 19TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI2003
, 2003
"... There is almost always a cost associated with acquiring training data. We consider the situation where the learner, with a fixed budget, may ‘purchase ’ data during training. In particular, we examine the case where observing the value of a feature of a training example has an associated cost, and t ..."
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Cited by 53 (4 self)
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, and the total cost of all feature values acquired during training must remain less than this fixed budget. This paper compares methods for sequentially choosing which feature value to purchase next, given the budget and user’s current knowledge of Naïve Bayes model parameters. Whereas active learning has
Results 1  10
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260,750