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Marginal Maximum Entropy Partitioning Yields Asymptotically Consistent Probability Density Functions
April 2001 (vol. 23 no. 4)
pp. 414-417

Abstract—The marginal maximum entropy criterion has been used to guide recursive partitioning of a continuous sample space. Although the criterion has been successfully applied in pattern discovery applications, its theoretical justification has not been clearly addressed. In this paper, it is shown that the basic marginal maximum entropy partitioning algorithm yields asymptotically consistent density estimates. This result supports the use of the marginal maximum entropy criterion in pattern discovery and implies that an optimal classifier can be constructed.

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Index Terms:
Marginal maximum entropy, recursive partitioning, pattern discovery, asymptotic optimal classification.
Citation:
Tom Chau, "Marginal Maximum Entropy Partitioning Yields Asymptotically Consistent Probability Density Functions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 414-417, April 2001, doi:10.1109/34.917576
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