Bounds on the Generalization Performance of Kernel Machine Ensembles

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We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel machines. Special cases considered are bagging and support vector machines. We present experimental results supporting the theoretical bounds, and describe characteristics of kernel machines ensembles suggested from the experimental findings. We also show how such ensembles can be used for fast training with very large datasets.

Luis Pérez-Breva, Massimiliano Pontil, Theo

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ICML 2000 | Kernel Machines | Machine Learning | Support Vector Machines |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2000
Where	ICML
Authors	Luis Pérez-Breva, Massimiliano Pontil, Theodoros Evgeniou, Tomaso Poggio

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Bounds on the Generalization Performance of Kernel Machine Ensembles

ICML 2000 | Kernel Machines | Machine Learning | Support Vector Machines |

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