Kernel principal component analysis
Advances in kernel methods
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Convex Optimization
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
A continuation method for semi-supervised SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
A regularization framework for multiple-instance learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Deterministic annealing for semi-supervised kernel machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semi-Supervised Learning
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Semi-supervised support vector machine (S3VM) attempts to learn a decision boundary that traverses through low data density regions by maximizing the margin over labeled and unlabeled examples. Traditionally, S3VM is formulated as a non-convex integer programming problem and is thus difficult to solve. In this paper, we propose the cutting plane semi-supervised support vector machine (CutS3VM) algorithm, to solve the S3VM problem. Specifically, we construct a nested sequence of successively tighter relaxations of the original S3VM problem, and each optimization problem in this sequence could be efficiently solved using the constrained concave-convex procedure (CCCP). Moreover, we prove theoretically that the CutS3VM algorithm takes time O(sn) to converge with guaranteed accuracy, where n is the total number of samples in the dataset and s is the average number of non-zero features, i.e. the sparsity. Experimental evaluations on several real world datasets show that CutS3VM performs better than existing S3VM methods, both in efficiency and accuracy.