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Bayesian Classification
, 1999
"... Bayesian classification addresses the classification problem by learning the distribution of instances given different class values. We review the basic notion of Bayesian classification, describe in some detail the naive Bayesian classifier, and briefly discuss some extensions. ..."
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Cited by 1 (0 self)
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Bayesian classification addresses the classification problem by learning the distribution of instances given different class values. We review the basic notion of Bayesian classification, describe in some detail the naive Bayesian classifier, and briefly discuss some extensions.
Bayesian Classification with Gaussian Processes
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for c = 1; : : : ; m. For a twoclass problem, the probability of class 1 given x is estimated by oe(y(x)), where oe(y) = 1=(1 + e ). A Gaussian process prior is placed on y(x), and is combined wi ..."
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Cited by 178 (1 self)
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We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for c = 1; : : : ; m. For a twoclass problem, the probability of class 1 given x is estimated by oe(y(x)), where oe(y) = 1=(1 + e ). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points.
Bayesian classification with correlation and inheritance
 In Proceedings of the 12th International Joint Conference on Artificial Intelligence
, 1991
"... The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass system searches for the ..."
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Cited by 34 (2 self)
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The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass system searches
On Bayesian Classification with Laplace Priors
"... We present a new classification approach, using a variational Bayesian estimation of probit regression with Laplace priors. Laplace priors have been previously used extensively as a sparsity inducing mechanism to perform feature selection simultaneously with classification or regression. However, co ..."
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Cited by 20 (0 self)
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We present a new classification approach, using a variational Bayesian estimation of probit regression with Laplace priors. Laplace priors have been previously used extensively as a sparsity inducing mechanism to perform feature selection simultaneously with classification or regression. However
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
Not so naive Bayesian classification
"... Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening ..."
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Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers. In extensive experiments this technique delivers comparable prediction accuracy to LBR and TAN with substantially improved computational efficiency.
Bayesian Interpolation
 Neural Computation
, 1991
"... Although Bayesian analysis has been in use since Laplace, the Bayesian method of modelcomparison has only recently been developed in depth. In this paper, the Bayesian approach to regularisation and modelcomparison is demonstrated by studying the inference problem of interpolating noisy data. T ..."
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Cited by 721 (17 self)
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Although Bayesian analysis has been in use since Laplace, the Bayesian method of modelcomparison has only recently been developed in depth. In this paper, the Bayesian approach to regularisation and modelcomparison is demonstrated by studying the inference problem of interpolating noisy data
Results 1  10
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101,783