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Dissimilarity in graph-based semisupervised classification (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Goldberg, Andrew B. |
| Description | Label dissimilarity specifies that a pair of examples probably have different class labels. We present a semi-supervised classification algorithm that learns from dissimilarity and similarity information on labeled and unlabeled data. Our approach uses a novel graphbased encoding of dissimilarity that results in a convex problem, and can handle both binary and multiclass classification. Experiments on several tasks are promising. 1 |
| File Format | |
| Language | English |
| Publisher Date | 2007-01-01 |
| Publisher Institution | Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS |
| Access Restriction | Open |
| Subject Keyword | Graph-based Semisupervised Classification Semi-supervised Classification Algorithm Convex Problem Label Dissimilarity Specifies Multiclass Classification Different Class Label Several Task Similarity Information |
| Content Type | Text |
| Resource Type | Article |
