[SciPy-User] Classification using neural networks
Wed Jul 25 14:17:23 CDT 2012
Last time I was current with machine learning (ca. 5-7 years ago), the standard advice for the first pass at any particular problem was "throw it at an SVM". I don't know if that's still the go-to consensus these days -- can anyone else weight in? (Does some kind of ensemble method routinely beat SVMs these days in the same way that SVMs were routinely beating neural networks in the early 2000s? I guess I should check out abstracts from recent NIPS conferences to find out...)
Anyway, my suggestion would be to use libSVM (which has handy command-line programs, or python bindings), either from the authors:
or as part of the scikits-learn package (which has other classification algorithms rolled in too):
Here is "A practical guide to support vector classification" by the authors of libSVM, which provides best practices for trying to train a SVM from new data:
Also, libSVM supports multi-class classification (via a one-against-rest strategy IIRC), which looks like it's what you'll need. Though I note that determining percentages of rock types from continuous input data is more of a mixture modeling task, rather than classification (or even regression). Maybe you'd want to look into fitting gaussian (or non-gaussian) mixture models. For this, scikits-learn has various tools also.
On Jul 25, 2012, at 2:47 PM, Nuttall, Brandon C wrote:
> I am looking for a method to do rock type identification using geophysical log data acquired from oil and gas wells. The geophysical log data are continuous recordings of a variety of bulk rock properties that change with the type and abundance of mineral constituents. What I want to do is classify the rock types (limestone, sandstone, shale, dolomite, etc) and their relative percentages. I remember reading an earlier SCIPY post about a neural network module that used training datasets to establish a classification scheme and then was run against unknown data. But, I can’t find the post and don’t remember the module name. Can anyone point me in the right direction?
> Brandon Nuttall, KRPG-1364
> Kentucky Geological Survey
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