Name
Abstract | ||
|---|---|---|
We present an algorithm for multiclass semi-supervised learning, which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised learning 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 semi-supervised 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 benchmark and real-world datasets show that the proposed algorithm performs better than the state-of-the-art boosting algorithms for multiclass semi-supervised learning, such as SemiBoost (Mallapragada et al., 2009) and RegBoost (Chen and Wang, 2011). |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patrec.2013.10.008 | Pattern Recognition Letters |
Keywords | Field | DocType |
novel multiclass loss function,binary classification problem,multiclass problem,algorithms use,multiclass classification problem,proposed algorithm,unlabeled data,margin cost,limited amount,multiclass classification,boosting,semi supervised learning | Semi-supervised learning,Binary classification,Pattern recognition,Computer science,Regularization (mathematics),Artificial intelligence,Boosting (machine learning),Labeled data,Machine learning,Multiclass classification | Journal |
Volume | ISSN | Citations |
37, | 0167-8655 | 10 |
PageRank | References | Authors |
0.49 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jafar Tanha | 1 | 50 | 4.17 |
| Maarten van Someren | 2 | 402 | 48.51 |
| Hamideh Afsarmanesh | 3 | 1890 | 291.69 |