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PKDD
2010
Springer

Entropy and Margin Maximization for Structured Output Learning

9 years 23 days ago
Entropy and Margin Maximization for Structured Output Learning
Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs). A generalized loss function is introduced, which jointly maximizes the entropy and the margin of the solution. The CRF and SSVM emerge as special cases of our framework. The probabilistic interpretation of large margin methods reveals insights about margin and slack rescaling. Furthermore, we derive the corresponding extensions for latent variable models, in which training operates on partially observed outputs. Experimental results for multiclass, linear-chain models and multiple instance learning demonstrate that the generalized loss can improve accuracy of the resulting classifiers.
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PKDD
Authors Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann
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