Maximum Entropy Models for Iteratively Identifying Subjectively Interesting 
Structure in Real-valued Data

Kontonasios, Kleanthis-Nikolaos; Vreeken, Jilles; De Bie, Tijl

doi:10.1007/978-3-642-40991-2_17

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Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-valued Data

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Vreeken, Jilles
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Kontonasios, K.-N., Vreeken, J., & De Bie, T. (2013). Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-valued Data. In H. Blockeel, K. Kersting, S. Nijssen, & F. Želenzný (Eds.), Machine Learning and Knowledge Discovery in Databases (pp. 256-271). Berlin: Springer. doi:10.1007/978-3-642-40991-2_17.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0015-1CCC-4

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