Results 1  10
of
4,073
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 ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
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
, 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 ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
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
, 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 ..."
Abstract
 Add to MetaCart
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
, 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 ..."
Abstract

Cited by 198 (0 self)
 Add to MetaCart
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
, 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 ..."
Abstract
 Add to MetaCart
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
 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 ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
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
 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 ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
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 ..."
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 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 ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
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
Wrapping the naive bayes classifier to relax the effect of dependences
 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 ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
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