Matrix analysis
Applied statistics: a first course
Applied statistics: a first course
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Generalization Performance of Classifiers in Terms of Observed Covering Numbers
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Semi-Supervised Kernel Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Semi-Supervised Learning
Towards a theoretical foundation for laplacian-based manifold methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE Transactions on Information Theory
Covering numbers for support vector machines
IEEE Transactions on Information Theory
Semiparametric Regression Using Student Processes
IEEE Transactions on Neural Networks
Learning with limited and noisy tagging
Proceedings of the 21st ACM international conference on Multimedia
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Semiparametric regression is an attractive solution for many practical problems. A nonlinear parametric model set, which is frequently encountered in practice, brings difficulties in complexity evaluation and control. On the other hand, it is quite common in practice that a mass of unlabeled samples is available, this fact suggests the possibility of applying a semisupervised regression method. Motivated by these facts, this paper proposes the manifold regularization based semisupervised semiparametric regression (MRBS^2R) method, which is characterized by introducing the manifold regularization (MR) technique in determining the parametric model. Generalization performance analysis shows that the generalization performance of the regression will be remarkably improved by introducing MR. Numerical experiments are performed to validate the proposed method.