We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of the approach in a number of industrial applications. Bayesian approach provides a principled way to handle the problem of overfitting, by averaging over all model complexities weighted by their posterior probability given the data sample. The approach also facilitates estimation of the confidence...

Several state-of-the-art techniques: a neural network, Bayesian neural network, support vector machine and naive Bayesian classi-er are experimentally evaluated in discriminating uorescence in-situ hybridization (FISH) signals. Highly-accurate classiication of signals from real data and artifacts of two cytogenetic probes (colours) is required for detecting abnormalities in the data. More than ...

Optimization and parameter estimation techniques have been employed for many years as a method of improving and exploring designs in numerous areas. As the designs of antennas and antenna arrays become more complex in nature, optimization techniques such as Bayesian estimation or genetic algorithms have become more necessary in the design process. These techniques provide methods for not only t...

A general problem in compositional data analysis is the unmixing of a composition into a series of pure endmembers. In its most complex version, one does neither know the composition of these endmembers, nor their relative contribution to each observed composition. The problem is particularly cumbersome if the number of endmembers is larger than the number of observed components. This contribut...

The von Mises distribution is a maximum entropy distribution. It corresponds to the distribution of an angle of a compass needle in a uniform magnetic eld of direction, , with concentration parameter,. The concentration parameter, , is the ratio of the eld strength to the temperature of thermal uctuations. Previously, we obtained a Bayesian estimator for the von Mises distribution parameters us...

This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In this paper, we shall demonstrate that by ...

In today world of internet, it is important to feedback the users based on what they demand. Moreover, one of the important tasks in data mining is classification. Today, there are several classification techniques in order to solve the classification problems like Genetic Algorithm, Decision Tree, Bayesian and others. In this article, it is attempted to classify researchers to “Expert” and “No...

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to ...