Volume :
37
Year :
2014
Page :
63-77
Source :
Pattern Recognition Letters
Format Published :
PDF
Descriptors :
Semi-supervised learning , Boosting , Multiclass classification
Descriptors - جزئيات :
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).
Call. No. :
EA 84
IndexDate :
1397/10/23
Indexer :
Dashagha
Title of Article :

Boosting for multiclass semi-supervised learning

RecordNumber :
85
Author/Authors :
Tanha, Jafar , Someren, Maarten van , Afsarmanesh, Hamideh
Author/Authors - جزئيات :
Link To Document :

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