Classification algorithms
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
On the exponential value of labeled samples
Pattern Recognition Letters
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Text classification in a hierarchical mixture model for small training sets
Proceedings of the tenth international conference on Information and knowledge management
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Continuous queries over data streams
ACM SIGMOD Record
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
B-EM: a classifier incorporating bootstrap with EM approach for data mining
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Information Theory - Part 2
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This paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy. This is significant for many applications such as image classification where obtaining classification labels is expensive, while large unlabeled examples are easily available. We investigate an Expectation Maximization (EM) algorithm for learning from labeled and unlabeled data. The reason why unlabeled data boosts learning accuracy is because it provides the information about the joint probability distribution. A theoretical argument shows that the more unlabeled examples are combined in learning, the more accurate the result. We then introduce B-EM algorithm, based on the combination of EM with bootstrap method, to exploit the large unlabeled data while avoiding prohibitive I/O cost. Experimental results over both synthetic and real data sets show that the proposed approach has a satisfactory performance.