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991
Boosting And Naive Bayesian Learning
, 1997
"... Although socalled "naive" Bayesian classification makes the unrealistic assumption that the values of the attributes of an example are independent given the class of the example, this learning method is remarkably successful in practice, and no uniformly better learning method is known. B ..."
Abstract

Cited by 81 (2 self)
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Although socalled "naive" Bayesian classification makes the unrealistic assumption that the values of the attributes of an example are independent given the class of the example, this learning method is remarkably successful in practice, and no uniformly better learning method is known
Naive Bayesian Classifier Committees
 Proceedings of the 10th European Conference on Machine Learning
, 1998
"... . The naive Bayesian classifier provides a very simple yet surprisingly accurate technique for machine learning. Some researchers have examined extensions to the naive Bayesian classifier that seek to further improve the accuracy. For example, a naive Bayesian tree approach generates a decision tree ..."
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Cited by 15 (1 self)
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. The naive Bayesian classifier provides a very simple yet surprisingly accurate technique for machine learning. Some researchers have examined extensions to the naive Bayesian classifier that seek to further improve the accuracy. For example, a naive Bayesian tree approach generates a decision
Naive Bayesian rough sets
 Proceedings of RSKT 2010, LNAI 6401
, 2010
"... Abstract. A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a binary classifier. The theory of rough sets provides a ternary classification method by approximating a set int ..."
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Cited by 2 (2 self)
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Abstract. A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a binary classifier. The theory of rough sets provides a ternary classification method by approximating a set
Adjusted probability naive Bayesian induction
 Proceedings of the Eleventh Australian Joint Conference on Artificial Intelligence
, 1998
"... Naive Bayesian classifiers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classification tasks is surprisingly competitive in comparison to more complex ..."
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Cited by 26 (12 self)
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Naive Bayesian classifiers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classification tasks is surprisingly competitive in comparison to more
Discriminative Naive Bayesian Classifiers
"... Discriminative classifiers such as Support Vector Machines (SVM) directly learn a discriminant function or a posterior probability model to perform classification. On the other hand, generative classifiers often learn a joint probability model and then use Bayes rules to construct a posterior cla ..."
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Cited by 1 (0 self)
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in various classification tasks, it is better to combine these two strategies. In this paper, we develop a method to train one of the popular generative classifiers, the Naive Bayesian classifier (NB) in a discriminative way. We name this new model as Discriminative Naive Bayesian classifier. We give
Continuous Naive Bayesian Classifications
"... Abstract. The most common model of machine learning algorithms involves two lifestages, namely the learning stage and the application stage. The cost of human expertise makes difficult the labeling of large sets of data for the training of machine learning algorithms. In this paper, we propose to c ..."
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application stage and without the privilege of any external feedback. The intuitive motivation and idea of this paradigm are elucidated, followed by explanations on how it differs from other learning models. Finally, empirical evaluation of Continuous Learning applied to the Naive Bayesian Classifier
Seminaive Bayesian Classification
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... The success and popularity of naive Bayes (NB) has led to a field of research exploring algorithms that seek to retain its numerous strengths while reducing error by alleviating the attribute interdependence problem. These algorithms can be categorized into five groups: those that apply conventional ..."
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. To provide a baseline for comparison, we also present comprehensive experimental results for Logistic Regression and LibSVM, a popular SVM implementation. In analyzing the results of these experiments we provide general recommendations for selection between seminaive Bayesian methods based
An Evaluation of Naive Bayesian AntiSpam Filtering
, 2000
"... It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk email ("spam"). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the ..."
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Cited by 165 (1 self)
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It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk email ("spam"). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate
Hierarchical Mixtures of Naive Bayesian Classifiers
"... Naive Bayesian 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 Bayesian classifiers by using the hierarchical m ..."
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Naive Bayesian 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 Bayesian classifiers by using the hierarchical
Naive Bayesian classifiers for ranking
 Proceedings of the 15th European Conference on Machine Learning (ECML2004
, 2004
"... Abstract. It is wellknown that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class probabilities is desired. For example, a ranking of customers in terms of the likelihood that they buy one’s produ ..."
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Cited by 11 (0 self)
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Abstract. It is wellknown that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class probabilities is desired. For example, a ranking of customers in terms of the likelihood that they buy one’s
Results 1  10
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991