Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
A continuation method for semi-supervised SVMs
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
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
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Existing semi-supervised learning methods are mostly based on either the cluster assumption or the manifold assumption. In this paper, we propose an integrated regularization framework for semi-supervised kernel machines by incorporating both the cluster assumption and the manifold assumption. Moreover, it supports kernel learning in the form of kernel selection. The optimization problem involves joint optimization over all the labeled and unlabeled data points, a convex set of basic kernels, and a discrete space of unknown labels for the unlabeled data. When the manifold assumption is incorporated, graph Laplacian kernels are used as the basic kernels for learning an optimal convex combination of graph Laplacian kernels. Comparison with related methods on the USPS data set shows very promising results.