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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Amayri, O. Bouguila, N. |
| Copyright Year | 2009 |
| Description | Author affiliation: Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Quebec, Canada (Amayri, O.; Bouguila, N.) |
| Abstract | The majority of used kernels in SVMs concern continuous data, and neglect the structure of the text. In contrast to classical kernels, we propose the use of various string kernels for spam filtering. On the other hand, data preprocessing is a vital part of text classification where the objective is to generate feature vectors usable by SVM kernels. We detail a feature mapping variant in text classification (TC) that yields improved performance for the standard SVM in filtering task. Furthermore, we propose an online active framework for spam filtering. |
| Starting Page | 337 |
| Ending Page | 340 |
| File Size | 387977 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424446728 |
| ISSN | 15301346 |
| DOI | 10.1109/ISCC.2009.5202287 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-05 |
| Publisher Place | Tunisia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Kernel Support vector machine classification Unsolicited electronic mail Text categorization Machine learning Frequency Information filtering Information filters Information systems Transductive Support Vector Machines Spam filtering Support Vector Machines Feature Mapping Online Active |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Mathematics Computer Networks and Communications Computer Science Applications Software |
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