2009, English, Article edition: Boosting Classifiers built from Different Subsets of Features Janodet, Jean-Christophe; Sebban, Marc; Suchier, Henri-Maxime

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Bookmark: http://trove.nla.gov.au/version/203498
Physical Description
  • text
Published
  • HAL - CCSD
  • 2009
Language
  • English

Edition details

Title
  • Boosting Classifiers built from Different Subsets of Features
Author
  • Janodet, Jean-Christophe
  • Sebban, Marc
  • Suchier, Henri-Maxime
Published
  • HAL - CCSD
  • 2009
Physical Description
  • text
Subjects
Notes
  • We focus on the adaptation of boosting to representation spaces composed of different subsets of features. Rather than imposing a single weak learner to handle data that could come from different sources (e.g., images and texts and sounds), we suggest the decomposition of the learning task into several dependent sub-problems of boosting, treated by different weak learners, that will optimally collaborate during the weight update stage. To achieve this task, we introduce a new weighting scheme for which we provide theoretical results. Experiments are carried out and show that our method works significantly better than any combination of independent boosting procedures.
  • HAL:hal-00403242, version 1
  • HAL:http:/​/​hal.archives-ouvertes.fr/​hal-00403242/​en/​
  • DOI:10.3233/​FI-2009-131
Language
  • English
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