C4.5: programs for machine learning
C4.5: programs for machine learning
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
Adaptive View Validation: A First Step Towards Automatic View Detection
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
A novel multi-view classifier based on Nyström approximation
Expert Systems with Applications: An International Journal
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A view validation algorithm has been shown to predict whether or not the views are sufficiently compatible for solving a particular learning task. But it only works when a natural split of features exists. If the split does not exist, it will fail to manufacture a feature split to build the best views. In this paper, we present a general algorithm CCFP (Correlation and Compatibility based Feature Partitioner) to automate multi-view detection. CCFP first labels the large amount of unlabeled examples using single view algorithm, then calculates the conditional SU (Symmetric Uncertainty) between every pair of features and the IG (Information Gain) of each feature given the examples labeled previously by single view algorithm with high-confidence predictions. According to the estimated values of SU and IG, all the features will be partitioned into two views that are low correlated, compatible and sufficient enough. The experiment results show that multi-view learner with views generated by CCFP outperforms learner with views generated by other means clearly.