Results 1 - 10 of 4,073

The indifferent naive bayes classifier.

by Jesús Cerquides , 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

Generalized Naive Bayes Classifiers

by Kim Larsen , 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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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 Naive Bayes Classifier

by Kenneth Benoit , 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

An empirical study of the naive bayes classifier

by I. Rish , 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

Hierarchical Mixtures of Naive Bayes Classifiers

by Marco A. Wiering , 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

Evolving Extended Naive Bayes Classifier

by Frank Klawonn, Plamen Angelov - 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

Naïve Bayes Classifiers for User Modeling

by Mia K. Stern, Joseph E. Beck, Beverly Park Woolf - 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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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

by Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier - PROCEEDINGS OF THE LWA 2006, LERNEN WISSENSENTDECKUNG ADAPTIVITÄT , 2006
"... Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class 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 ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
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 one-against-all binarization is not equivalent to a regular Naive

Incremental Discretization for Naïve-Bayes Classifier

by Jingli Lu, Ying Yang, Geoffrey I. Webb
"... Abstract. Naïve-Bayes 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. Naïve-Bayes 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

Wrapping the naive bayes classifier to relax the effect of dependences

by Jose Carlos Cortizo , Ignacio Giraldez , Mari Cruz Gaya - In IDEAL 2007 , 2007
"... Abstract. The Naive Bayes Classifier is based on the (unrealistic) assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease in the presence of interdependent attributes. In spite of this, in recent years, Naive Bayes classifie ..."
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Abstract. The Naive Bayes Classifier is based on the (unrealistic) assumption of independence among the values of the attributes given the class value. Consequently, its effectiveness may decrease in the presence of interdependent attributes. In spite of this, in recent years, Naive Bayes
Results 1 - 10 of 4,073