document: Extracting query modifications from nonlinear SVMs When searching the WWW , users often desire results restricted to a particular document category . Ideally , a user would be able to filter results with a text classifier to minimize false positive results ; however , current search engines allow only simple query modifications . To automate the process of generating effective query modifications , we introduce a sensitivity analysis-based method for extracting rules from nonlinear support vector machines . The proposed method allows the user to specify a desired precision while attempting to maximize the recall . Our method performs several levels of dimensionality reduction and is vastly faster than searching the combination feature space ; moreover , it is very effective on real-world data .
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