Learning efficient error correcting output codes for large hierarchical multi-class problems

Abstract : We describe a new approach for dealing with hierarchical classification with a large number of classes. We build on Error Correcting Output Codes and propose two algorithms that learn compact, binary, low dimensional class codes from a similarity information between classes. This allows building classification algorithms that performs similarly or better than the standard and performing one-vs-all approach, with much lower inference complexity.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01286789
Contributor : Lip6 Publications <>
Submitted on : Friday, March 11, 2016 - 1:55:36 PM
Last modification on : Thursday, March 21, 2019 - 2:16:05 PM

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  • HAL Id : hal-01286789, version 1

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Moustapha Cissé, Thierry Artières, Patrick Gallinari. Learning efficient error correcting output codes for large hierarchical multi-class problems. Workshop on Large Scale Hierarchical Classification (at ECML), Sep 2011, Athens, Greece. pp.37-48. ⟨hal-01286789⟩

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