Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks
We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential function...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 39(2017), 9 vom: 15. Sept., Seite 1839-1852 |
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| Format: | Article en ligne |
| Langue: | English |
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2017
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |