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<Paper uid="N04-1039">
  <Title>Exponential Priors for Maximum Entropy Models</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
  </Section>
class="xml-element"></Paper>