Journal of Optimization Theory and Applications
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
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
The Journal of Machine Learning Research
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Brief paper: A set-membership state estimation algorithm based on DC programming
Automatica (Journal of IFAC)
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
DC models for spherical separation
Journal of Global Optimization
A fast quasi-Newton method for semi-supervised SVM
Pattern Recognition
Binary classification via spherical separator by DC programming and DCA
Journal of Global Optimization
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This paper investigate a class of semi-supervised support vector machines ($$\text{ S }^3\mathrm{VMs}$$S3VMs) with arbitrary norm. A general framework for the $$\text{ S }^3\mathrm{VMs}$$S3VMs was first constructed based on a robust DC (Difference of Convex functions) program. With different DC decompositions, DC optimization formulations for the linear and nonlinear $$\text{ S }^3\mathrm{VMs}$$S3VMs are investigated. The resulting DC optimization algorithms (DCA) only require solving simple linear program or convex quadratic program at each iteration, and converge to a critical point after a finite number of iterations. The effectiveness of proposed algorithms are demonstrated on some UCI databases and licorice seed near-infrared spectroscopy data. Moreover, numerical results show that the proposed algorithms offer competitive performances to the existing $$\text{ S }^3\mathrm{VM}$$S3VM methods.