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Type: Artigo de evento
Title: Multi-class From Binary: Divide To Conquer
Author: Rocha A.
Goldenstein S.
Abstract: Several researchers have proposed effective approaches for binary classification in the last years. We can easily extend some of those techniques to multi-class. Notwithstanding, some other powerful classifiers (e.g., S VMs) are hard to extend to multi-class. In such cases, the usual approach is to reduce the multi-class problem complexity into simpler binary classification problems (divide-and-conquer). In this paper, we address the multi-class problem by introducing the concept of affine relations among binary classifiers (dichotomies), and present a principled way to find groups of high correlated base learners. Finally, we devise a strategy to reduce the number of required dichotomies in the overall multi-class process.
Citation: Visapp 2009 - Proceedings Of The 4th International Conference On Computer Vision Theory And Applications. , v. 1, n. , p. 323 - 330, 2009.
Rights: fechado
Date Issue: 2009
Appears in Collections:Unicamp - Artigos e Outros Documentos

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