Learning from measurements in exponential families

  title={Learning from measurements in exponential families},
  author={Percy Liang and Michael I. Jordan and Dan Klein},
Given a model family and a set of unlabeled examples, one could either label specific examples or state general constraints---both provide information about the desired model. In general, what is the most cost-effective way to learn? To address this question, we introduce measurements, a general class of mechanisms for providing information about a target model. We present a Bayesian decision-theoretic framework, which allows us to both integrate diverse measurements and choose new measurements… CONTINUE READING
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Techniques of variational analysis

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