Efficiency of learning with imperfect supervision
Pattern Recognition
An alternative stochastic supervisor in discriminant analysis
Pattern Recognition
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
The use of unlabeled data to improve supervised learning for text summarization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
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
A comparison of some error estimates for neural network models
Neural Computation
Semi-supervised learning with an imperfect supervisor
Knowledge and Information Systems
Data Clustering with Partial Supervision
Data Mining and Knowledge Discovery
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Least Square Transduction Support Vector Machine
Neural Processing Letters
Semi-supervised document classification with a mislabeling error model
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Semi-supervised classification and noise detection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Learning aspect models with partially labeled data
Pattern Recognition Letters
Extracting initial and reliable negative documents to enhance classification performance
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
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This paper investigates a new approach for training discriminant classifiers when only a small set of labeled data is available together with a large set of unlabeled data. This algorithm optimizes the classification maximum likelihood of a set of labeled-unlabeled data, using a variant form of the Classification Expectation Maximization (CEM) algorithm. Its originality is that it makes use of both unlabeled data and of a probabilistic misclassification model for these data. The parameters of the label-error model are learned together with the classifier parameters. We demonstrate the effectiveness of the approach on four data-sets and show the advantages of this method over a previously developed semi-supervised algorithm which does not consider imperfections in the labeling process.