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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Ensemble Learning with Supervised Kernels
verfasst von : Kari Torkkola, Eugene Tuv
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
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Kernel-based methods have outstanding performance on many machine learning and pattern recognition tasks. However, they are sensitive to kernel selection, they may have low tolerance to noise, and they can not deal with mixed-type or missing data. We propose to derive a novel kernel from an ensemble of decision trees. This leads to kernel methods that naturally handle noisy and heterogeneous data with potentially non-randomly missing values. We demonstrate excellent performance of regularized least square learners based on such kernels.