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The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers...
As the Internet grows rapidly, finding desirable information becomes a tedious and time consuming task. Topic-specific web crawlers, as utopian solutions, tackle this issue through traversing the Web and collecting information related to the topic of interest. In this regard, various methods are proposed. Nevertheless, they hardly consider desired sense of the given topic which would certainly play...
By rapid growth of the Internet, finding desirable information would be a challenging and time consuming task. In order to tackle this issue, focused crawlers, as the ideal solution, through mining of the Web, help us to find web pages closely relevant to the desired information. For this purpose, a variety of methods are devised and implemented. Nonetheless, the majority of these methods do not favor...
Methods of semantic relatedness are essential for wide range of tasks such as information retrieval and text mining. This paper, concerned with these automated methods, attempts to improve Gloss Vector semantic relatedness measure for more reliable estimation of relatedness between two input concepts. Generally, this measure by considering frequency cut-off for big rams tries to remove low and high...
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