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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