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A flexible true plurigaussian code for spatial facies simulations   总被引:3,自引:0,他引:3  
The current forms of plurigaussian simulation have serious limitations for applications to large numbers of geological facies, or units, which have complex contact relations. In this paper the authors present a true plurigaussian simulation (PGS) method, which can be applied in a simple way to any number of geological facies by using any number of Gaussians. A recursive technique is used for multi-dimensional integration of the Gaussian functions, which forms the major part of the PGS computation. A binary, dynamic contact matrix (DCM) is used to specify the contact relations among the facies; this method has proved to be simple, flexible and capable of dealing with general, complex contact relations. A method for incorporating into PGS multivariate correlations among any number of random variables is also included. A simulated example is used to demonstrate the application of the generalised PGS. This example shows that PGS is more robust to under-sampling than traditional direct indicator simulation.  相似文献
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Feature selection is one of the major problems in an intrusion detection system (IDS) since there are additional and irrelevant features. This problem causes incorrect classification and low detection rate in those systems. In this article, four feature selection algorithms, named multivariate linear correlation coefficient (MLCFS), feature grouping based on multivariate mutual information (FGMMI), feature grouping based on linear correlation coefficient (FGLCC), and feature grouping based on pairwise MI, are proposed to solve this problem. These algorithms are implementable in any IDS. Both linear and nonlinear measures are used in the sense that the correlation coefficient and the multivariate correlation coefficient are linear, whereas the MI and the multivariate MI are nonlinear. Least Square Support Vector Machine (LS-SVM) as an intrusion classifier is used to evaluate the selected features. Experimental results on the KDDcup99 and Network Security Laboratory-Knowledge Discovery and Data Mining (NSL) datasets showed that the proposed feature selection methods have a higher detection and accuracy and lower false-positive rate compared with the pairwise linear correlation coefficient and the pairwise MI employed in several previous algorithms.  相似文献
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