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Domain adaptation (DA) aims to eliminate the difference between the distribution of labeled source domain on which a classifier is trained and that of unlabeled or partly labeled target domain to which the classifier is to be applied. Compared with the semi-supervised domain adaptation where some labeled data from target domain is utilized to help train the classifier, the unsupervised domain adaptation...
In this paper, we propose a polymorphic viruses detection method based on support vector machine (SVM) in the Windows platform. Our approach rests on an analysis using the Windows API calling sequence that reflects the behavior of a particular piece of code. By extracting the variable-length system calling sequence or patterns in system calling sequence as the SVM training data and employing the ReliefF...
In this paper, we present a novel semi-supervised and interactive concept learning algorithm for scene recognition by local semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to model the semantic content of images. The basic idea of the semantic modeling is to classify local image regions into semantic concept classes such as water,...
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