The nature of statistical learning theory
The nature of statistical learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Using Unlabelled Data for Text Classification through Addition of Cluster Parameters
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learner's Self-Assessment: A Case Study of SVM for Information Retrieval
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Rule discovery from textual data based on key phrase patterns
Proceedings of the 2004 ACM symposium on Applied computing
Discovering Classification from Data of Multiple Sources
Data Mining and Knowledge Discovery
ECML '07 Proceedings of the 18th European conference on Machine Learning
Classification techniques with minimal labelling effort and application to medical reports
International Journal of Data Mining and Bioinformatics
Acquisition of a classification model for a risk search system from unbalanced textual examples
International Journal of Business Intelligence and Data Mining
Towards modeling threaded discussions using induced ontology knowledge
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Active learning with multiple views
Journal of Artificial Intelligence Research
An e-mail analysis method based on text mining techniques
Applied Soft Computing
Exploratory Consensus of Hierarchical Clusterings for Melanoma and Breast Cancer
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Unsupervised and supervised learning in cascade for petroleum geology
Expert Systems with Applications: An International Journal
Clustering and categorization of Brazilian portuguese legal documents
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
Web classification of conceptual entities using co-training
Expert Systems with Applications: An International Journal
CoNet: feature generation for multi-view semi-supervised learning with partially observed views
Proceedings of the 21st ACM international conference on Information and knowledge management
High performance query expansion using adaptive co-training
Information Processing and Management: an International Journal
The impact of semi-supervised clustering on text classification
Proceedings of the 17th Panhellenic Conference on Informatics
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In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for training the other predictor in the next round. Both predictors are support vector machines, one trained using data from the original feature space, the other trained with new features that are derived by clustering both the labelled and unlabelled data. Hence, unlike standard co-training methods, our method does not require a priori the existence of two redundant views either of which can be used for classification, nor is it dependent on the availability of two different supervised learning algorithms that complement each other.We evaluated our method with two classifiers and three text benchmarks: WebKB, Reuters newswire articles and 20 NewsGroups. Our evaluation shows that our co-training technique improves text classification accuracy especially when the number of labelled examples are very few.