The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Deterministic annealing for semi-supervised kernel machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Deterministic annealing for semi-supervised kernel machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernel selection forl semi-supervised kernel machines
Proceedings of the 24th international conference on Machine learning
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Cuts3vm: a fast semi-supervised svm algorithm
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Transductive Support Vector Machine by Ensembling
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Least Square Transduction Support Vector Machine
Neural Processing Letters
Clustering with local and global regularization
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Is unlabeled data suitable for multiclass SVM-based web page classification?
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
A bi-fuzzy progressive transductive support vector machine(BFPTSVM) algorithm
Expert Systems with Applications: An International Journal
A novel transductive learning algorithm based on multi-agent-system
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A general learning framework using local and global regularization
Pattern Recognition
Gradient descent optimization of smoothed information retrieval metrics
Information Retrieval
Semi-supervised Bayesian ARTMAP
Applied Intelligence
A fast quasi-Newton method for semi-supervised SVM
Pattern Recognition
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Toward supervised anomaly detection
Journal of Artificial Intelligence Research
A second order cone programming approach for semi-supervised learning
Pattern Recognition
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
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Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.