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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lichen Liang Cherkassky, V. |
| Copyright Year | 2007 |
| Description | Author affiliation: Minnesota Univ., Minneapolis (Lichen Liang; Cherkassky, V.) |
| Abstract | This paper investigates a new learning setting recently introduced by Vapnik (2006) that takes into account a known structure of the training data to improve generalization performance. This setting is a special case of a new inference technology known as learning with hidden information(Vapnik, 2006) suitable for many real-life applications with sparse high-dimensional data. We first briefly describe an extension of SVM called SVMgamma+ (Vapnik, 2006) that is associated with this new learning setting, and verify its effectiveness using a synthetic data set. Then we demonstrate the effectiveness of SVMgamma+ on a difficult real-life problem: detection of cognitive states from fMRI images obtained from different subjects. These empirical results show that the SVMgamma+ approach achieves improved inter-subject generalization vs standard SVM technology. |
| Starting Page | 495 |
| Ending Page | 499 |
| File Size | 280038 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371006 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data analysis Training data Machine learning Support vector machines Inference algorithms Neural networks Predictive models State estimation Testing Learning systems |
| Content Type | Text |
| Resource Type | Article |
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