We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c = 1, o, m. For a twoclass problem, the probability of class one given x is estimated by s(y(x)), where s(y) = 1/(1 + ey ). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points. We provide a Bayesian treatment, integrating over u...

This paper describes AutoClass H, a program for automatically discovering (inducing) classes from a database, based on a Bayesian statistical technique which automatically determines the most probable number of classes, their probabilistic descriptions, and the probability that each object is a member of each class. AutoClass has been tested on several large, real databases and has discovered p...

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 ...

one of the most important objectives of any economic system is to achieve to low and stable inflation and sustained economic growth. in this study, first, bayesian causal is indentified effective factors on inflation and then using bayesian causal network and determining prior probabilities and posterior probabilities in different scenarios,it is discussed the impacts of this factors on inflati...

Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method. The Bayesian method includes 5 algorithms, including AODE, BayesNet, HNB, NaviveBayes, WAODE. The...

Development of an automated method for classifying digital images using a combination of image quality modification and Bayesian classification is the subject of this thesis. The specific example is classification of images obtained by monitoring molten plastic in an extruder. These images were to be classified into two groups: the “with particle” (WP) group which showed contaminant particles a...

This paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial logit likelihood as well as the likelihood related to the multiclass Support Vector Machine (SVM) model. It is shown that our proposed Bayesian classification models with multiple...

The constant increase in the amount and resolution of remotely sensed imagery necessitates development of intelligent systems for automatic processing and classification. We describe a Bayesian framework that uses spatial information for classification of high-resolution images. First, spectral and textural features are extracted for each pixel. Then, these features are quantized and are used t...

In the conventional and relatively simple image processing techniques are most important task in the field of medical imaging. In this work to provide information about segmentation and classification methods that are very important for medical image processing. Ultrasound is unique in its ability to image patient anatomy and physiology in real time, providing an important, rapid and non-invasi...