Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Document Categorization and Query Generation on the World Wide WebUsing WebACE
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Semi-supervised support vector machines
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Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Exploiting unlabeled data in ensemble methods
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Semi-Supervised Self-Training of Object Detection Models
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Multi-Class Learning by Smoothed Boosting
Machine Learning
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The Journal of Machine Learning Research
Semi-Supervised Boosting for Multi-Class Classification
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Journal of Artificial Intelligence Research
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Semi-supervised multi-class Adaboost by exploiting unlabeled data
Expert Systems with Applications: An International Journal
Disagreement-Based Co-training
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
A theory of multiclass boosting
The Journal of Machine Learning Research
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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).