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From "Claudia Grieco" <gri...@crmpa.unisa.it>
Subject R: Help with Mahout Classification
Date Thu, 03 Feb 2011 11:55:11 GMT
Thanks for your help.
I've tried implementing several "boolean" classifiers ("sport" or "not sport") but they don't
seem to work very well (they tend to classify everything as "positive" or everything as "negative").
Do you think that for it to return meaningful classifications, the model should be trained
with an almost equal amount of "positive" and "negative" data? 

-----Messaggio originale-----
Da: Ted Dunning [mailto:ted.dunning@gmail.com] 
Inviato: lunedì 31 gennaio 2011 16.43
A: user@mahout.apache.org
Oggetto: Re: Help with Mahout Classification

For 50 categories, yes.  For 5000, no.

If you have 50 categories, you probably also have inter-category constraints
(i.e. cannot be about football but not sports).

To deal with that, training 50 independent models and then training 50
models that get to use the output of the first 50 models as inputs might
help (haven't tried this sort of thing for several years).


On Mon, Jan 31, 2011 at 2:55 AM, Claudia Grieco <grieco@crmpa.unisa.it>wrote:

> Hi,
> Just one more question about the SGD classifier.
> When you say " train one classifier per category" it means that for every
> possible tag (ex. sport) I should create a classifier that classifies it as
> "sport" or "not sport"? (sorry, English is not my first language)
> Do you think this approach is feasible for many categories (let's say 50)?
> Thanks again
> Claudia
>
> -----Messaggio originale-----
> Da: Ted Dunning [mailto:ted.dunning@gmail.com]
> Inviato: venerdì 14 gennaio 2011 17.32
> A: user@mahout.apache.org
> Oggetto: Re: Help with Mahout Classification
>
> If you don't have truly massive volumes, then SGD is almost certainly a
> better choice because it is simpler.
>
> If you have more than 10 million training examples *per*model* and
> *after*downsampling* then you should consider alternatives but even up to
> about 50 million training examples, SGD will do very well.  SGD is
> currently
> also mostly appropriate for sparse feature vectors.
>
> Having multiple categories isn't a big deal.  The simplest solution is to
> train a classifier per category.  There are more advanced arrangements,
> though.  For instance, you can train one classifier per category (the first
> level models), then train another classifier per category where the inputs
> are the outputs of the first level models.  Which techniques will help is
> highly dependent on your particular problem.
>
> On Fri, Jan 14, 2011 at 7:10 AM, Claudia Grieco <grieco@crmpa.unisa.it
> >wrote:
>
> > Do you think SGD will be a better choice? New documents are added to the
> > training set very often and documents can belong to more than one
> category
> > (ex. "sport", "italy")
>
>


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