Machine Learning - Special issue on inductive transfer
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
A Multitask Learning Approach to Face Recognition Based on Neural Networks
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Semantic Features for Multi-view Semi-supervised and Active Learning of Text Classification
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Semi-Supervised Learning
Multiple-View Multiple-Learner Semi-Supervised Learning
Neural Processing Letters
Multi-view laplacian support vector machines
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Multi-source transfer learning with multi-view adaboost
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Recent developments on semi-supervised learning have witnessed the effectiveness of using multiple views, namely integrating multiple feature sets to design semi-supervised learning methods. However, the so-called multiview semi-supervised learning methods require the availability of multiple views. For many problems, there are no ready multiple views, and although the random split of the original feature sets can generate multiple views, it is definitely not the most effective approach for view construction. In this paper, we propose a feature selection approach to construct multiple views by means of genetic algorithms. Genetic algorithms are used to find promising feature subsets, two of which having maximum classification agreements are then retained as the best views constructed from the original feature set. Besides conducting experiments with single-task support vector machine (SVM) classifiers, we also apply multitask SVM classifiers to the multi-view semi-supervised learning problem. The experiments validate the effectiveness of the proposed view construction method.