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16

ICML
2005
IEEE

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Multi-instance tree learning

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Multi-instance tree learning

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We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree learners in a few crucial, well-motivated details. Experiments on synthetic and real-life datasets confirm the beneficial effect of these differences and show that the resulting system outperforms the existing multi-instance decision tree learners.

Hendrik Blockeel, David Page, Ashwin Srinivasan

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Decision Tree Learners | Dietterich Et Al | ICML 2005 | Machine Learning | Multi-instance Tree Learners |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2005
Where	ICML
Authors	Hendrik Blockeel, David Page, Ashwin Srinivasan

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