Hello,
I watched both excellent tutorials from PyCon 2013 on YouTube and although
without strong background in statistics, encouraged by this fast food and
Andy's Machine Learning Cheat Sheet on screen, I thought to try something
out.
I have large set of signals with extracted spectral signatures for each
(it's not astronomy). I classify these signals manually as I already tried
in the past to detect some simple correlation between the signatures and
classification groups, but I didn't find anything reliable. I could try
some signal processing and heavy statistics, but that's far from trivial
and I'm not sure I have right potential to go there.
My problem to get started is this - I don't have all target classification
groups upfront, so new signal may not belong to any of already existing
classification groups, but introduce new. This is causing me trouble to
find the route and get started. If what I said is not intelligible, I'll
try to describe it differently - imagine digits example that comes with
sklearn; now imagine that I can classify only couple of digits (0,1,2,3,4)
and train model on limited set of already classified digits, and now when I
probe other digit (like 5,6,7,8,9), I want model to be able to distinguish
each of those in separate groups accordingly. Does this make sense? Is it
possible at all?
Thanks in advance
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