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Maximum entropy classifier is mostly used for Natural Language Processing, in which the datas are discrete. I learned the formalism from the paper A maximum entropy approach to natural language processing and understood most of it.

My question is, how to apply the Maximum Entropy principle to do the classification on a continuous data set?

I am asking for some sources where I can learn the formalism, not the software packages.

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  • $\begingroup$ Pardon my ignorance, but what is a continuous dataset? $\endgroup$ – gregory_britten Jun 24 '14 at 11:54
  • $\begingroup$ @gregory_britten , the value of the features are real floating number. Maybe there are some better phrases, sorry about the confusion. $\endgroup$ – EricCoding Jun 25 '14 at 14:46
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Finally I found this paper,

[1] D. Yu, L. Deng, and A. Acero, Pattern Recognit. Lett. 30, 1295–1300 (2009).

That is exactly what I need.

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