Add to ORKGNaive 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. This paper presents a variant of the Naive Bayes method, in which the original training set is augmented in the following fashion: Leave-One-Out procedure is applied over the training set, and incorrectly classified instances according to Naive Bayes model are duplicated. The augmented dataset is used to induce the model. The motivation behind this idea is that giving more importance to hard instances (in this case, duplicating them) might contribute to make the model more accurate over that subset of the instance space. We have tested this algorithm over 41 UCI d...
Recent work in supervised learning has shown that a surpris-ingly simple Bayesian classifier with st...
Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, ...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
Many algorithms have been proposed for the machine learning task of classification. One of the simpl...
AbstractNaive Bayes is a well-known and studied algorithm both in statistics and machine learning. B...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
Can we evolve better training data for machine learning algorithms? To investigate this question we ...
Many algorithms have been proposed for the machine learning task of classification. One of the simpl...
Many algorithms have been proposed for the machine learning task of classication. One of the simples...
The naïve Bayes classifier is a simple form of Bayesian classifiers which assumes all the features a...
Naive Bayes models have been successfully used in classification problems where the class variable i...
Abstract. We investigate why discretization can be effective in naive-Bayes learning. We prove a the...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...
Bayesian classiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent perf...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
Recent work in supervised learning has shown that a surpris-ingly simple Bayesian classifier with st...
Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, ...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
Many algorithms have been proposed for the machine learning task of classification. One of the simpl...
AbstractNaive Bayes is a well-known and studied algorithm both in statistics and machine learning. B...
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in ...
Can we evolve better training data for machine learning algorithms? To investigate this question we ...
Many algorithms have been proposed for the machine learning task of classification. One of the simpl...
Many algorithms have been proposed for the machine learning task of classication. One of the simples...
The naïve Bayes classifier is a simple form of Bayesian classifiers which assumes all the features a...
Naive Bayes models have been successfully used in classification problems where the class variable i...
Abstract. We investigate why discretization can be effective in naive-Bayes learning. We prove a the...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...
Bayesian classiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent perf...
. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with s...
Recent work in supervised learning has shown that a surpris-ingly simple Bayesian classifier with st...
Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, ...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...