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» Kernels for Multi--task Learning

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SDM
2004
SIAM

218views Data Mining» more SDM 2004»

Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data

13 years 9 months ago

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This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...

Ashok N. Srivastava

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ICML
2007
IEEE

179views Machine Learning» more ICML 2007»

A kernel path algorithm for support vector machines

14 years 8 months ago

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The choice of the kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. The past few years have se...

Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky

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NIPS
2007

155views Information Technology» more NIPS 2007»

Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes

13 years 9 months ago

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We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We ﬁrst learn a deep generative model of the unlabeled da...

Ruslan Salakhutdinov, Geoffrey E. Hinton

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COLT
2007
Springer

112views Machine Learning» more COLT 2007»

Learning Languages with Rational Kernels

14 years 1 months ago