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An AdaBoost Algorithm for Multiclass Semi-Supervised Learning
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2013 |
| Abstract | We present an algorithm for multiclass SemiSupervised learning which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semisupervised algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems which is not optimal. We propose a multiclass semisupervised boosting algorithm that solves multiclass classification problems directly. The algorithm is based on a novel multiclass loss function consisting of the margin cost on labeled data and two regularization terms on labeled and unlabeled data. Experimental results on a number of UCI datasets show that the proposed algorithm performs better than the stateof-the-art boosting algorithms for multiclass semi-supervised learning. Keywords-Semi-Supervised Learning; boosting; multiclass classification |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://pure.uva.nl/ws/files/1806830/118115_CameraReadyICDMtex.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
