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Ensembles of Budgeted Kernel Support Vector Machines for Parallel Large Scale Learning

Julien-Charles Lévesque, Christian Gagné and Robert Sabourin

Abstract - In this work, we propose to combine multiple budgeted kernel support vector machines (SVMs) trained with stochastic gradient descent (SGD) in order to exploit large databases and parallel computing resources. The variance induced by budget restrictions of the kernel SVMs is reduced through the averaging of predictions, resulting in greater generalization performance. The variance of the trainings results in a diversity of predictions, which can help explain the better performance. Finally, the proposed method is intrinsically parallel, which means that parallel computing resources can be exploited in a straightforward manner.

Bibtex:

@inproceedings{Lévesque1017,
author    = { Julien-Charles Lévesque and Christian Gagné and Robert Sabourin },
title     = { Ensembles of Budgeted Kernel Support Vector Machines for Parallel Large Scale Learning },
booktitle = { NIPS Workshop on Big Learning: Advances in Algorithms and Data Management },
address   = { Lake Tahoe, USA },
year      = { 2013 },
month     = { December }
}

Dernière modification: 2013/11/05 par jclev7

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