an Artificial Neural Network classifier is a nonparametric classifier. It does not need any priori knowledge regarding the statistical distribution of the class in a giver selected data Source. While, neural network can be trained to distinguish the criteria used to classify easily in a generalized manner that allows successful classification the newly arrived inputs not used during training. T...

the aim of this study was to estimate suspended sediment by the ann model, dt with cart algorithm and different types of src, in ten stations from the lorestan province of iran. the results showed that the accuracy of ann with levenberg-marquardt back propagation algorithm is more than the two other models, especially in high discharges. comparison of different intervals in models showed that r...

Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its generalizations often do not converge when applied to hard, real-life inference tasks. While it is widely recognized that the scheduling of messages in these algorithms may have significant consequences, this issue rema...

In this paper, we first demonstrate the interest of the Loopy Belief Propagation algorithm to train and use a simple alignment model where the expected marginal values needed for an efficient EM-training are not easily computable. We then improve this model with a distortion model based on structure conservation.

We construct two graphical models for skin detection from a large collection of labeled data. Within it, parameter estimation is eradicated by tree approximations and the belief propagation algorithm permits to obtain exact and fast solution for skin probability at pixel locations. We then assess the performance on the Compaq database.

Davis 1] has investigated the properties of the Waltz propagation algorithm with interval labels in continuous domains. He shows that in most cases, the algorithm does not achieve arc consistency, and furthermore is subject to innnite iterations. In this paper, I show that the main reason for Davis' negative results lies in the way he formulates the propagation rule for the Waltz algorithm. For...

In this paper, simple modifications to the pseudospectral time-domain (PSTD) algorithm that offer dispersion compensation are presented. In a homogeneous lossless medium, the compensation offers an exact solution for one-dimensional (1D) propagation. For 2D and 3D propagation, the proposed method can compensate dispersion for a central frequency in any direction. The techniques are then extende...

After many Internet-scale worm incidents in recent years, it is clear that a simple self-propagation worm can quickly spread across the Internet. And every worm incidents can cause severe damage to our society. So it is necessary to build a system that can detect the presence of worm as quickly as possible. This paper first analyzes the worm’s framework and its propagation model. Then, we descr...

In this paper we consider the problem of reducing the computational difficulties in g-coherence checking and propagation of imprecise conditional probability assessments. We review some theoretical results related with the linear structure of the random gain in the betting criterion. Then, we propose a modified version of two existing algorithms, used for g-coherence checking and propagation, w...

We propose centralized algorithm of data distribution in the unicast p2p networks. Good example of such networks are meshes of WWW and FTP mirrors. Simulation of data propagation for different network topologies is performed and it is shown that proposed method performs up to 200% better then common approaches.