In this comparative study, the Bayesian and a neural network (the HLVQ) approaches are used to classify multispectral LANDSAT images. The studied area contains several agricultural classes (wheat, flax,....). Some classes are found to be non homogeneous and thus are divided in this study into several subclasses. The Gaussian assumption needed by the Bayesian classifier is thus justified by this...

A new Bayesian formulation is developed for nonlinear support vector machines (SVMs), based on a Gaussian process and with the SVM hinge loss expressed as a scaled mixture of normals. We then integrate the Bayesian SVM into a factor model, in which feature learning and nonlinear classifier design are performed jointly; almost all previous work on such discriminative feature learning has assumed...

A popular method for creating an accurate classifier from a set of training data is to train several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. However, the simple Bayesian classifier has much broader applicability than previously thought. Besides its high classification accuracy, it also has ...

River segmentation using satellite image contextual information and Bayesian classifier Paria Yousefi, H. A. Jalab, R. W. Ibrahim, N. F. Mohd Noor, M. N. Ayub & A. Gani To cite this article: Paria Yousefi, H. A. Jalab, R. W. Ibrahim, N. F. Mohd Noor, M. N. Ayub & A. Gani (2016) River segmentation using satellite image contextual information and Bayesian classifier, The Imaging Science Journal, ...

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural normalization in the likelihood function, and then follow standard Gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement...

A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classi...