Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Noise-tolerant learning, the parity problem, and the statistical query model
Journal of the ACM (JACM)
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
The Journal of Machine Learning Research
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating topic transition in topic detection and tracking algorithms
Expert Systems with Applications: An International Journal
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Text classification based on multi-word with support vector machine
Knowledge-Based Systems
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A hybrid generative/discriminative approach to semi-supervised classifier design
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Exponential family hybrid semi-supervised learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Semi-Supervised Learning
A 'non-parametric' version of the naive Bayes classifier
Knowledge-Based Systems
Combining active learning and semi-supervised learning to construct SVM classifier
Knowledge-Based Systems
Inter-training: Exploiting unlabeled data in multi-classifier systems
Knowledge-Based Systems
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Training methods for machine learning are often characterized as being generative or discriminative. We present a new co-training style algorithm which employs a generative classifier (Naive Bayes) and a discriminative classifier (Support Vector Machine) as base classifiers, to take advantage of both methods. Furthermore, we introduce a pair of weight parameters to balance the impact of labeled and pseudo-labeled data, and define a hybrid objective function to tune their values during co-training. The final prediction is given by the combination of base classifiers, and we define a pseudo-validation set to regulate their weight. Additionally, we present a strategy of pseudo-labeled data selecting to deal with the class imbalance problem. Experimental results on six datasets show that our method performs much better in practice, especially when the amount of labeled data is small.