An image classification scheme using Naïve Bayes Classifier is proposed in this paper. The proposed Naive Bayes Classifier-based image classifier can be considered as the maximum a posteriori decision rule. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time when compared to conventional supervised or unsupervised learning algorithms. Compreh...

It is well known that in unidentifiable models, the Bayes estimation has the advantage of generalization performance to the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an empirical Bayes approach where a part of the parameters are regarded as hyperparameters, which we call a subspace B...

Bayes belief networks and influence diagrams are tools for constructing coherent probabilistic representations of uncertain expert opinion. The construction of such a network with about 30 nodes is used to illustrate a variety of techniques which can facilitate the process of structuring and quantifying uncertain relationships. These include some generalizations of the "noisy OR gate" concept. ...

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-lineari...

Markov Logic Networks (MLNs) are a prominent statistical relational model that have been proposed as a unifying framework for statistical relational learning. As part of this unification, their authors proposed methods for converting other statistical relational learners into MLNs. For converting a first order Bayes net into an MLN, it was suggested to moralize the Bayes net to obtain the struc...

Bayesian networks are powerful tools for decision and reasoning under uncertainty. A very simple form of these networks is called naive Bayes, which is particularly efficient for learning and inference tasks. This paper offers an experimental study of the use of naive Bayes in intrusion detection. We show that eventhough they have a simple structure, naive Bayes provide satisfactory results. We...

Although at first sight probabilistic networks and fuzzy clustering seem to be disparate areas of research, a closer look reveals that they can both be seen as generalizations of naive Bayes classifiers. If all attributes are numeric (except the class attribute, of course), naive Bayes classifiers often assume an axis-parallel multidimensional normal distribution for each class as the underlyin...

A Naive (or Idiot) Bayes network is a network with a single hypothesis node and several observations that are conditionally independent given the hypothesis. We recently surveyed a number of members of the UAI community and discovered a general lack of understanding of the implications of the Naive Bayes assumption on the kinds of problems that can be solved by these networks. It has long been ...