Intrinsic Classification by MML—the Snob Program (1994)
| Venue: | Proc. Seventh Australian Joint Conf. Artificial Intelligence |
| Citations: | 5 - 0 self |
BibTeX
@INPROCEEDINGS{Wallace94intrinsicclassification,
author = {Christopher S. Wallace and David L. Dowe},
title = {Intrinsic Classification by MML—the Snob Program},
booktitle = {Proc. Seventh Australian Joint Conf. Artificial Intelligence},
year = {1994},
pages = {37--44},
publisher = {World Scientific}
}
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Abstract
Abstract: We provide a brief overview ofMinimum Message Length (MML) inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)). We then outline how MML is used for statistical parameter estimation, and how the MML intrinsic classification program, Snob (Wallace and Boulton (1968), Wallace (1986), Wallace (1990)) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with model selection in intrinsic classification. We mention here the most recent extensions to Snob, permitting Poisson and von Mises circular distributions. We also survey some applications of Snob (albeit briefly), and further provide some documentation on how the user can guide Snob’s search through various models of the given data to try to obtain that model whose message length is a minimum.
