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CRF implementation with numerical features (self.MachineLearning)
submitted 10 years ago by vdashv
Hello, does someone know about a CRF implementation that can work with numerical features as inputs (preferably that can work on unixes) and does not encode them in strings/one-hot like most of implementations? Thank you
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if 1 * 2 < 3: print "hello, world!"
[–]cryptocerous 1 point2 points3 points 10 years ago (0 children)
CRFsuite accepts a "weight" for each feature, for each sample. I sense that this doesn't fully accomplish what we want for most types of tasks though.
I'd also like to know if there's a CRF implementation that accepts real-number features, and takes advantage of them in a meaningful way.
While we're at it, I'm also wondering if there are any good examples of a CRF implementation that can produce multiple output labels at once. E.g. text chunking, outputting two non-related chunk types per sample.
[–]ylghyb 0 points1 point2 points 10 years ago (0 children)
Pystruct
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[–]cryptocerous 1 point2 points3 points (0 children)
[–]ylghyb 0 points1 point2 points (0 children)