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
Making large-scale support vector machine learning practical
Advances in kernel methods
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
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
Learning word normalization using word suffix and context from unlabeled data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth 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
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multi-view Semi-supervised Learning: An Approach to Obtain Different Views from Text Datasets
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005
Discriminative models for semi-supervised natural language learning
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Active learning with multiple views
Journal of Artificial Intelligence Research
Convex Mixture Models for Multi-view Clustering
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Learning to integrate web taxonomies
Web Semantics: Science, Services and Agents on the World Wide Web
Exploiting tag and word correlations for improved webpage clustering
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
Journal of Biomedical Informatics
Extracting dimensions for OLAP on multidimensional text databases
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
The Journal of Machine Learning Research
Estimation of mixture models using Co-EM
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multi-view discriminative sequential learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
Feature-Correlation based multi-view detection
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Understanding user intent in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Multi-view learning via probabilistic latent semantic analysis
Information Sciences: an International Journal
An application of the self-organizing map to multiple view unsupervised learning
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering
ACM Transactions on Intelligent Systems and Technology (TIST)
A hybrid generative/discriminative method for semi-supervised classification
Knowledge-Based Systems
Co-training on multi-view unlabelled data
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Inter-training: Exploiting unlabeled data in multi-classifier systems
Knowledge-Based Systems
Researcher homepage classification using unlabeled data
Proceedings of the 22nd international conference on World Wide Web
Co-metric: a metric learning algorithm for data with multiple views
Frontiers of Computer Science: Selected Publications from Chinese Universities
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Proceedings of the 7th ACM international conference on Web search and data mining
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Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Co-EM outperforms co-training for many problems, but it requires the underlying learner to estimate class probabilities, and to learn from probabilistically labeled data. Therefore, co-EM has so far only been studied with naive Bayesian learners. We cast linear classifiers into a probabilistic framework and develop a co-EM version of the Support Vector Machine. We conduct experiments on text classification problems and compare the family of semi-supervised support vector algorithms under different conditions, including violations of the assumptions underlying multi-view learning. For some problems, such as course web page classification, we observe the most accurate results reported so far.