Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
Semi-Supervised Mixture-of-Experts Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Information Sciences: an International Journal
Face Detection Using Mixture of MLP Experts
Neural Processing Letters
Wavelet/mixture of experts network structure for EEG signals classification
Expert Systems with Applications: An International Journal
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
Adaptive mixtures of local experts
Neural Computation
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
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The mixture-of-experts (ME) models can be useful to solve complicated classification problems in real world. However, in order to train the ME model with not only labeled data but also unlabeled data which are easier to come, a new learning algorithm that considers characteristics of the ME model is required. We proposed global-local co-training (GLCT), the hybrid training method of the ME model training method for supervised learning (SL) and the co-training, which trains the ME model in semi-supervised learning (SSL) manner. GLCT uses a global model and a local model together since using the local model only shows low accuracy due to lack of labeled training data. The models enlarge the labeled data set from the unlabeled one and are trained from it by supplementing each other. To evaluate the method, we performed experiments using benchmark data sets from UCI machine learning repository. As the result, GLCT confirmed the feasibility of itself. Moreover, a comparison experiments to show the excellences of GLCT showed better performance than the other alternative method.