Chapter

Machine Learning and Knowledge Discovery in Databases

Volume 6323 of the series Lecture Notes in Computer Science pp 83-98

Entropy and Margin Maximization for Structured Output Learning

  • Patrick PletscherAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, ETH Zürich
  • , Cheng Soon OngAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, ETH Zürich
  • , Joachim M. BuhmannAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, ETH Zürich

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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.