A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection
A large vector-angular region and margin (LARM) approach is presented for novelty detection based on imbalanced data. The key idea is to construct the largest vector-angular region in the feature space to separate normal training patterns; meanwhile, maximize the vector-angular margin between the su...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2016-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/1658758 |