Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
Semi-supervised Gaussian process classifiers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extensions of the informative vector machine
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Discriminative deep belief networks for visual data classification
Pattern Recognition
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Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.