Neighborhood Property--Based Pattern Selection for Support Vector Machines
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Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
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A kernel path algorithm for support vector machines
Proceedings of the 24th international conference on Machine learning
Discriminant kernel and regularization parameter learning via semidefinite programming
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Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Learning dissimilarities by ranking: from SDP to QP
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Multi-class Discriminant Kernel Learning via Convex Programming
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An efficient kernel matrix evaluation measure
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On the Stability and Bias-Variance Analysis of Kernel Matrix Learning
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L2 regularization for learning kernels
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Expert Systems with Applications: An International Journal
Employing multiple-kernel support vector machines for counterfeit banknote recognition
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The Journal of Machine Learning Research
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The Journal of Machine Learning Research
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This paper addresses the problem of choosing a kernel suitable for estimation with a support vector machine, hence further automating machine learning. This goal is achieved by defining a reproducing kernel Hilbert space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional.We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.