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24th Pan-Hellenic Conference on Informatics, 2020
Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the ...
Vangjel Kazllarof, Sotiris B. Kotsiantis
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Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the ...
Vangjel Kazllarof, Sotiris B. Kotsiantis
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Less naive Bayes spai detection
2007 Information Theory and Applications Workshop, 2007We consider a binary classification problem with a feature vector of high dimensionality. Spam mail filters are a popular example hereof. A naive Bayes filter assumes conditional independence of the feature vector components. We use the context tree weighting method as an application of the minimum description length principle to allow for dependencies
Yang, Hongming +2 more
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Naives Bayes-Klassifikationsverfahren
2020Das naive Bayes-Verfahren ist ein weiteres Klassifikationsverfahren. Es wird zunachst fur jede Klasse die Wahrscheinlichkeit geschatzt, mit der ein Objekt zu dieser Klasse gehort, wobei die aus der Statistik bekannte Bayes-Formel fur bedingte Wahrscheinlichkeiten benutzt wird.
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Probabilistic Fuzzy Naive Bayes
2015 Brazilian Conference on Intelligent Systems (BRACIS), 2015Bayesian networks are probabilistic graphical models capable of modeling statistical uncertainty and are widely applied in many classification problems. Specifically, Naive Bayesian networks are largely used due to their simple, naive structure, while still producing precise results.
Gabriel Moura, Mauro Roisenberg
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A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering, 2009Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB).
null Liangxiao Jiang +2 more
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2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018
Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants.
Liangjun Yu +3 more
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Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants.
Liangjun Yu +3 more
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2020
The data mining methods we have been learning all favor numerical data: linear regression, LDA, k-means clustering, logistic regression, and K-NN. Naive Bayes favors categorical data, however. Because of its simplicity, naive Bayes data mining method is much more efficient compared to other data mining methods, while its performance can still match ...
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The data mining methods we have been learning all favor numerical data: linear regression, LDA, k-means clustering, logistic regression, and K-NN. Naive Bayes favors categorical data, however. Because of its simplicity, naive Bayes data mining method is much more efficient compared to other data mining methods, while its performance can still match ...
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1999
Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The iterative Bayes begins with the distribution tables built by the naive Bayes.
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Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The iterative Bayes begins with the distribution tables built by the naive Bayes.
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Generalized Naive Bayes Classifiers
ACM SIGKDD Explorations Newsletter, 2005This 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 predictive analysis.
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