in this research, an iterative approach is employed to recognize and classify control chart patterns. to do this, by taking new observations on the quality characteristic under consideration, the maximum likelihood estimator of pattern parameters is first obtained and then the probability of each pattern is determined. then using bayes’ rule, probabilities are updated recursively. finally, when...

in order to assess the satellite data for soil investigation, aster digital data 20 june 2006, field study and phisiochemical properties of soil, were analyzed. all landcover classes including soils are classified based onmorphological and physico-chemical characteristics. images were geocorrected and photomorphic units were selected based upon visual interpretation and sampling in study area. ...

ELECT Joint detection and maximum-likelihood (ML) classification of linear modulations based on observations collected over an unknown flatfading additive Gaussian noise channel is considered. It is assumed that some of the observations are subject to data failures, in which case the receiver acquires only noise. Expectation–maximisation algorithm is employed to compute the ML estimates of the ...

Two classification algorithms that rely on both spectral and textural information are presented and compared. The first is a standard maximum-likelihood classification procedure with a texture "band" added to the spectral band set. The second is a pattern matching algorithm which integrates the spectral and spatial characteristics of the data in recognizing a user-specified training pattern. Th...

Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and artificial neural network methods in terms of classification accuracy. Our results show th...

This paper addresses the problem of classification of digital modulations. The proposed solution uses the Bayes classifier, which is implemented by the Markov chain Monte Carlo scheme. In the proposed implementation, classifications in presence of phase and frequency offsets as well as residual filtering effects coming from imperfect channel equalization are considered. The proposed approach ha...

This paper proposes a new ARGWishart(Adaptive Region Growing-Wishart) classification algorithm for digital images. It integrates the adaptive region growing algorithm and Wishart maximum likelihood classification algorithm for difficulties that arise from selecting training samples, and instability of the final classification accuracy on PolSAR(Polarimetric Synthetic Aperture Radar) image. At f...

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2001) formalized the definition of two types of direct effects (natural and controlled) under the counterfactual framework. The efficient scores (under a nonparametric model) for t...