The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Multi-kernel regularized classifiers
Journal of Complexity
A DC-programming algorithm for kernel selection
ICML '06 Proceedings of the 23rd international conference on Machine learning
Feature space perspectives for learning the kernel
Machine Learning
The Journal of Machine Learning Research
Value Regularization and Fenchel Duality
The Journal of Machine Learning Research
Kernel selection forl semi-supervised kernel machines
Proceedings of the 24th international conference on Machine learning
Learning to combine distances for complex representations
Proceedings of the 24th international conference on Machine learning
Learning from incomplete data with infinite imputations
Proceedings of the 25th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Extended kernel recursive least squares algorithm
IEEE Transactions on Signal Processing
Robust label propagation on multiple networks
IEEE Transactions on Neural Networks
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Learning Translation Invariant Kernels for Classification
The Journal of Machine Learning Research
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
A new scheme to learn a kernel in regularization networks
Neurocomputing
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Learning bounds for support vector machines with learned kernels
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Algorithms for learning kernels based on centered alignment
The Journal of Machine Learning Research
Learning with infinitely many features
Machine Learning
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We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider this problem when the kernel is in the convex hull of basic kernels, for example, Gaussian kernels which are continuously parameterized by a compact set. We show that there always exists an optimal kernel which is the convex combination of at most m+1 basic kernels, where m is the sample size, and provide a necessary and sufficient condition for a kernel to be optimal. The proof of our results is constructive and leads to a greedy algorithm for learning the kernel. We discuss the properties of this algorithm and present some preliminary numerical simulations.