The statistical analysis of compositional data
The statistical analysis of compositional data
Discriminant analysis with a stochastic supervisor
Pattern Recognition
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
Learning from a mixture of labeled and unlabeled examples with parametric side information
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
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
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
Learning Classification with Both Labeled and Unlabeled Data
ECML '02 Proceedings of the 13th European Conference on Machine Learning
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
Semi-supervised Clustering by Seeding
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
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
A comparison of some error estimates for neural network models
Neural Computation
Semi-supervised learning with explicit misclassification modeling
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IEEE Transactions on Information Theory - Part 2
Knowledge and Information Systems
A Modified Semi-Supervised Learning Algorithm on Laplacian Eigenmaps
Neural Processing Letters
Preventing error propagation in semi-supervised learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Real-life applications may involve huge data sets with misclassified or partially classified training data. Semi-supervised learning and learning in the presence of label noise have recently emerged as new paradigms in the machine learning community to cope with this kind of problems. This paper describes a new discriminant algorithm for semi-supervised learning. This algorithm optimizes the classification maximum likelihood (CML) of a set of labeled–unlabeled data, using a discriminant extension of the Classification Expectation Maximization algorithm. We further propose to extend this algorithm by modeling imperfections in the estimated class labels for unlabeled data. The parameters of this label-error model are learned together with the semi-supervised classifier parameters. We demonstrate the effectiveness of the approach using extensive experiments on different datasets.