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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Semi-supervised locally discriminant projection for classification and recognition
Knowledge-Based Systems
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Owing to its wide applicability, semi-supervised learning is an attractive method for using unlabeled data in classification. Applying a new smoothing strategy to a class of continuous semi-supervised support vector machines (S^3VMs), this paper proposes a class of smooth S^3VMs (S^4VMs) without adding new variables and constraints to the corresponding S^3VMs. Moreover, a general framework for solving the S^4VMs is constructed based on robust DC (difference of convex functions) programming. Furthermore, DC optimization algorithms (DCAs) for solving the S^4VMs are investigated. The resulting DCAs converge and only require solving one linear or quadratic program at each iteration. Numerical experiments on some real-world databases demonstrate that the proposed smooth S^3VMs are feasible and effective, and have comparable results as other S^3VMs.