2012/7/9 Philipp Singer <[email protected]>:
> Am 09.07.2012 13:47, schrieb Peter Prettenhofer:
>> Hi,
>
> Hey!
>>
>> some quick thoughts:
>>
>> - if you use a multinomial Naive Bayes classifier (aka a language
>> model) you can fit a background model on the large dataset and use
>> that to smooth the model fitted on the smaller dataset.
>
> That's a nice idea. Is there a simple way to try this out fast in
> scikit-learn?
not really, you would need to write you own estimator that simply
takes the two MultinomialNaiveBayes models as arguments and does the
smoothing on predict(_proba); shouldn't be too much code though. If
you do a simple linear interpolation the following should suffice::
def predict_proba(X):
return self.lambda * self.foreground.predict_proba(X) + (1.0 -
self.lambda) * self.background.predict_proba(X)
You could estimate lambda via EM but I'd rather tune using CV.
>>
>> - you should look at the domain adaptation / multi-task learning
>> literature - this might fit your setting better than traditional
>> semi-supervised learning.
>
> Thanks, I will look into that.
>>
>> best,
>> Peter
>
> Regards,
> Philipp
>>
>> 2012/7/9 Gael Varoquaux<[email protected]>:
>>> Hi,
>>>
>>> You can try setting this as a semi-supervised learning problem and using
>>> label propagation:
>>>
>>> http://scikit-learn.org/stable/modules/label_propagation.html#label-propagation
>>>
>>> HTH,
>>>
>>> G
>>>
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>>
>>
>>
>
>
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Peter Prettenhofer
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