Visual reconstruction
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
A Fast and Accurate Progressive Algorithm for Training Transductive SVMs
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Least Square Transduction Support Vector Machine
Neural Processing Letters
Keepin' it real: semi-supervised learning with realistic tuning
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
DCA based algorithms for feature selection in semi-supervised support vector machines
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in 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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An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For margin-based loss functions, one can view this approach as attempting to learn low-density separators. However, this is a hard optimization problem to solve in typical semi-supervised settings where unlabeled data is abundant. The popular Transductive SVM algorithm is a label-switching-retraining procedure that is known to be susceptible to local minima. In this paper, we present a global optimization framework for semi-supervised Kernel machines where an easier problem is parametrically deformed to the original hard problem and minimizers are smoothly tracked. Our approach is motivated from deterministic annealing techniques and involves a sequence of convex optimization problems that are exactly and efficiently solved. We present empirical results on several synthetic and real world datasets that demonstrate the effectiveness of our approach.