Xuefeng Xian

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Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding(More)
For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a(More)
—An ever increasing amount of valuable information is stored in web databases, "hidden" behind search interfaces. A new application area emerge for information retrieval and integration. There may be hundreds or thousands of web databases providing data of relevance to a particular domain on the web. So a primary challenge to internet-scale hidden web(More)
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