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ICML
2004
IEEE

Multi-task feature and kernel selection for SVMs

13 years 23 days ago
Multi-task feature and kernel selection for SVMs
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is advantageous when multiple classification tasks and differently labeled datasets exist over a common input space. Different datasets can mutually reinforce a common choice of representation or relevant features for their various classifiers. We derive a multi-task representation learning approach using the maximum entropy discrimination formalism. The resulting convex algorithms maintain the global solution properties of support vector machines. However, in addition to multiple SVM classification/regression parameters they also jointly estimate an optimal subset of features or optimal combination of kernels. Experiments are shown on standardized datasets.
Tony Jebara
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Tony Jebara
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