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NAACL
2004

Exponential Priors for Maximum Entropy Models

9 years 2 months ago
Exponential Priors for Maximum Entropy Models
Maximum entropy models are a common modeling technique, but prone to overfitting. We show that using an exponential distribution as a prior leads to bounded absolute discounting by a constant. We show that this prior is better motivated by the data than previous techniques such as a Gaussian prior, and often produces lower error rates. Exponential priors also lead to a simpler learning algorithm and to easier to understand behavior. Furthermore, exponential priors help explain the success of some previous smoothing techniques, and suggest simple variations that work better.
Joshua Goodman
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NAACL
Authors Joshua Goodman
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