Hello,
I am trying to train a text classifier that can classify words and attach
labels to each of them. I have a MULTI-CLASS classifier (Linear SVM). The
classifier works well on a small training data. The problem arrives when I
use my actual training data to run the classifier. one of my labelled set
(lets say A) has a huge number of samples in it (~500) and the the other
labelled sets (say B, C, D, E ~5 to 30) have very less samples when
compared. Now this is wher it gets weird. Even if i enter an exact match
from set A, it is labelled with B/C/D/E. I have tried changing the weights
to 'auto' but no effect. Should I be looking at other algorithms? I have no
clue on how to proceed.
Thank you for your suggestions!
Thanks and Regards
--
Abhiram Koneru
Graduate Research Assistant
Clemson University
136 Fluor Daniel Building
Clemson, SC 29631
Email: [email protected] Ph no: (864)643-9672
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