A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Efficient computations for large least square support vector machine classifiers
Pattern Recognition Letters
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Optimizing resources in model selection for support vector machine
Pattern Recognition
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
Exponential family hybrid semi-supervised learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
Genetic algorithm–based training for semi-supervised SVM
Neural Computing and Applications
Pruning error minimization in least squares support vector machines
IEEE Transactions on Neural Networks
An improved conjugate gradient scheme to the solution of least squares SVM
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
Semisupervised Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
Semisupervised Learning Using Bayesian Interpretation: Application to LS-SVM
IEEE Transactions on Neural Networks
Combining active learning and semi-supervised learning to construct SVM classifier
Knowledge-Based Systems
A second order cone programming approach for semi-supervised learning
Pattern Recognition
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In this paper, we propose to reinforce the Self-Training strategy in semi-supervised mode by using a generative classifier that may help to train the main discriminative classifier to label the unlabeled data. We call this semi-supervised strategy Help-Training and apply it to training kernel machine classifiers as support vector machines (SVMs) and as least squares support vector machines. In addition, we propose a model selection strategy for semi-supervised training. Experimental results on both artificial and real problems demonstrate that Help-Training outperforms significantly the standard Self-Training. Moreover, compared to other semi-supervised methods developed for SVMs, our Help-Training strategy often gives the lowest error rate.