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With the growth of Android platform malicious application, the seperation of malicious application from nonmalicious application has become challenging. In recent years, a combination of static analysis and dynamic analysis of the idea is very popular. However, it is very costly for dynamic analysis to achieve high coverage. In this article we present an efficient, lightweight and behavior-based architecture...
With the development of computer network technology, Sina micro logging platform have become the birthplace of the hot spots and important route of transmission. Due to the complexity of the network information, sometimes, it is necessary to control the spread of some of microblogging. The forwarding prediction system can make contribution to build a better microblogging environment. The used dataset...
In efficiency analysis of weapon system, in order to capture and represent the decision maker's preferences and then to select the most desirable alternative, sensitivity analysis method of operational effectiveness based on LS-SVM is proposed. Firstly, the principle of effectiveness evaluation method based on LS-SVM is discussed. Secondly, to extract learning samples from the MADM problem, an approach...
The source number estimation is a basic problem in the smart antenna technology. Some classic estimation algorithms have been developed in past twenty years like `AIC', `MDL', hypothesis test (`HPY'), Gerschgorin Radii (`GDE'), etc .But the estimation error will be great in the circumstances of low S/N, small sample with these algorithms. This paper develops a novel method based on support vector...
Decision directed acyclic graph support vector machine (DDAGSVM) has been proposed to extend SVM from binary classification problems to multi-class classifications. But the generalization ability is subject to the structure of DDAG. To improve the classification accuracy, a novel separability measure is defined based on Karush-Kuhn-Tucher (KKT) condition, and an improved DDAGSVM has been given. The...
As a preprocessing method of data mining with SVM, feature selection can eliminate irrelevant or redundant attributes and increase the density of samples in feature spaces that can improve classification performance. In the field of financial time series pattern recognition, the study of feature selection has been receiving increasing attention. Different from other studies, this work use two new...
Based on traditional SVM, prior knowledge support vector machine (P-SVM) introduces application-oriented metrics into the training set to express expert knowledge. Developing with SLT theory, it is a new classification and prediction method established on firm mathematical foundation. Also, SVM provides the best solution of classification and prediction of limited sample set. In this paper, we introduce...
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