I have only one comment as of now:
On Tue, Nov 26, 2013 at 9:02 PM, Abhi <[email protected]> wrote:
> >.Please go through a paper "Ensemble of Exemplar-SVMs for Object Detection
> and Beyond" by Tomasz Malisiewicz (ICCV 2011)
> Thanks, will go through the paper.
>
> >During testing, a special calibration techniques used to choose the best
> output. On terms of calibration, differently sized templates will produce
> different scores so you have to do something like Platt's method.
>
>
> > Why do you want to do that?
> > To be more specific, how do you quantify success of your task?
> > By definition, probabilities are "normalized" in the sense that the
> > are guaranteed to live in the [0-1] range. However the classifier
> > models can be predict arbitrarily bad probabilities. For instance a
> > badly trained or badly parameterized binary classifier could predict 0
> > proba for the positive class 100% of the time.
>
>
> The following sums the problem:
> > AA[Classifiers are trained on similar type datasets, difference being
> their
> sizes and the way each result might be used after classification]. I am
> using SGDClassifier to train the individual classifiers, and need to
> choose
> the best amongst them. But as I understand I would need to normalize first
> before comparing them and was not sure how to calibrate them as such. Any
> pointers to would be helpful.
> - BBI would quantify a most similar document as a successful choice from
> the
> results of ensemble of classifiers. I was a bit confused on how to
> weight the predictions from each classifier so as to compare their
> values.
>
>>>>I think these are the similar problem that are addressed in the
paper I mentioned.
The only difference being that in your context its text data where as in
the paper its
images. But notion of calibrating multiple classifiers for the same
output is similar.
> - The motivation for having multiple classifiers was mostly due to the
> logical separation of the dataset documents' property in my problem.
> [From the implementation perspective the training multiple classifiers
> saves on time (~7 min vs ~17 min) and space (2.2G vs 12G)]
> --[Apologies on the duplicate thread, seems that it might take more than
> 6hrs for the post to show up]
>
> Thanks,
> Abhi
>
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--
Warm Regards
Yogesh Karpate
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