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Use of semantic content is one of the important tasks in image analysis, which needs to be addressed for improving image retrieval effectiveness. We present a method to assign multiple keywords to image using SVMs. Images are divided into three-level regions called global image, semi-global images and sub-images. For
In this paper, reclassification for the current classification through K-means would be implemented based on the feedback of Web usage mining in order to improve the accuracy of news recommendation and convergence of classification. It could extract most relative keywords and eliminate the disturbance of multi-vocal
Users of search engines interact with the system using different size and type of queries. Current search engines perform well with keyword queries but are not for verbose queries which are too long, detailed, or are expressed in more words than are needed. The detection of verbose queries may help search engines to
accuracy than individual classifiers. The maximum accuracy was got by enhancing the ensemble with an additional automatically generated domain specific class wise keyword list. Use of this system gave us greater than 4 percent improvement over the techniques of just using the ensemble classifier. A further improvement in
In text categorization, vectorizing a document by probability distribution is an effective dimension reduction way to save training time. However, the data sets that share many common keywords between categories affect the classification performance seriously. To address that problem, firstly, we conduct an effective
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