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Spotlight
Agreement-Based Learning
Percy Liang · Dan Klein · Michael Jordan
The learning of probabilistic models with many hidden variables and non-decomposable dependencies is an important but challenging problem. In contrast to traditional approaches based on approximate inference in a single intractable model, our approach is to train a set of tractable component models by encouraging them to agree on the hidden variables. This allows us to capture non-decomposable aspects of the data while still maintaining tractability. We exhibit an objective function for our approach, derive EM-style algorithms for parameter estimation, and demonstrate their effectiveness on three challenging real-world learning tasks.
Author Information
Percy Liang (Stanford University)
Dan Klein (UC Berkeley)
Michael Jordan (UC Berkeley)
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2007 Poster: Agreement-Based Learning »
Wed Dec 5th 06:30 -- 06:40 PM Room None
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