@CONFERENCE\{IMM2005-03997,
    author       = "R. E. Madsen and D. Kauchak and C. Elkan",
    title        = "Approximating The Dirichlet Compound Multinomial Distribution",
    year         = "2005",
    month        = "dec",
    keywords     = "{DCM,} Dirichlet, Polya, Text mining",
    booktitle    = "Neural Information Processing Systems",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3997-full.html",
    abstract     = "We investigate the Dirichlet compound multinomial (DCM), which has
recently been shown to be a good model for word burstiness in documents.
We provide a number of conceptual explanations that account for
these recent results. We then derive an exponential family approximation
of the {DCM} that is substantially faster to train, while still producing
similar probabilities and classification performance. We also investigate
Fisher kernels using the {DCM} model for generating distributionally
based similarity scores. Initial experiments show promise for this type of
similarity method."
}