Optimizing nondecomposable loss functions in structured prediction

Optimizing nondecomposable loss functions in structured prediction

We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as Fβ score (natural language processing), i...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 35(2013), 4 vom: 21. Apr., Seite 911-24
1. Verfasser: Ranjbar, Mani (VerfasserIn)
Weitere Verfasser: Lan, Tian, Wang, Yang, Robinovitch, Steven N, Li, Ze-Nian, Mori, Greg
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2013
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Journal Article Research Support, Non-U.S. Gov't