Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1145/511446.511488 | WWW |
Keywords | Field | DocType |
desire result,simple query modification,nonlinear support vector machine,sensitivity analysis-based method,false positive result,current search engine,nonlinear svms,effective query modification,combination feature space,dimensionality reduction,search engine,support vector machine,feature space,false positive,sensitivity analysis | Data mining,Nonlinear system,Dimensionality reduction,Computer science,Web query classification,Artificial intelligence,Classifier (linguistics),Feature vector,World Wide Web,Search engine,Query expansion,Support vector machine,Machine learning | Conference |
ISBN | Citations | PageRank |
1-58113-449-5 | 19 | 1.55 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gary W. Flake | 1 | 229 | 15.72 |
| Eric J. Glover | 2 | 600 | 73.63 |
| Steve Lawrence | 3 | 6194 | 872.30 |
| C. Lee Giles | 4 | 11154 | 1549.48 |