On the exponential value of labeled samples
Pattern Recognition Letters
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
The covering number in learning theory
Journal of Complexity
Improving Short-Text Classification using Unlabeled Data for Classification Problems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
The Journal of Machine Learning Research
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
The Journal of Machine Learning Research
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning
The Journal of Machine Learning Research
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
Error bounds of multi-graph regularized semi-supervised classification
Information Sciences: an International Journal
IEEE Transactions on Information Theory - Part 2
Capacity of reproducing kernel spaces in learning theory
IEEE Transactions on Information Theory
Graph-Based Semi-Supervised Learning and Spectral Kernel Design
IEEE Transactions on Information Theory
Semi-supervised learning based on high density region estimation
Neural Networks
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Semisupervised learning has been of growing interest over the past years and many methods have been proposed. While existing semisupervised methods have shown some promising empirical performances, their development has been based largely on heuristics. In this paper, we investigate semisupervised multicategory classification with an imperfect mixture density model. In the proposed model, the training data come from a probability distribution, which can be modeled imperfectly by an identifiable mixture distribution. Furthermore, we propose a semisupervised multicategory classification method and establish its generalization error bounds. The theoretical analysis illustrates that the proposed method can utilize unlabeled data effectively and can achieve fast convergence rate.