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|Type:||Artigo de evento|
|Title:||Multi-class From Binary: Divide To Conquer|
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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