Thanks Eibe. You're right.

Fernando

On Sat, Sep 17, 2016 at 6:11 PM, Eibe Frank <eibe@waikato.ac.nz> wrote:
SVMs can cope quite well with a large number of attributes, in particular, linear SVMs. However, it obviously doesn't make sense to add useless predictors just to make the set of attributes larger.

Cheers,
Eibe

> On 17 Sep 2016, at 22:06, Fernando Lavin <fernandolavin78@gmail.com> wrote:
>
> Thanks Eibe, bust the length of the this attribute (not class) how better it can be  (ie., the long, the better)?
>
> On Sat, Sep 17, 2016 at 6:04 PM, Eibe Frank <eibe@waikato.ac.nz> wrote:
> If you only have one attribute (plus a class attribute that you want to predict), it's probably overkill to use an SVM classifier.
>
> Every instance in the data must have the same set of attributes from the SVMs in WEKA, if that's what you are asking.
>
> Cheers,
> Eibe
>
> > On 17 Sep 2016, at 21:55, Fernando Lavin <fernandolavin78@gmail.com> wrote:
> >
> > I meant that, for  feature construction for activity recognition, should the length of feature vector to be feed for SVM classifier necessarily with attribute (column) of the data?
> >
> > Fernando
> >
> > On Sat, Sep 17, 2016 at 5:51 PM, Eibe Frank <eibe@waikato.ac.nz> wrote:
> > Not quite sure what you are asking. Can you perhaps rephrase the question and/or give an example?
> >
> > Cheers,
> > Eibe
> >
> > > On 17 Sep 2016, at 18:46, Fernando Lavin <fernandolavin78@gmail.com> wrote:
> > >
> > > Hi all,
> > >
> > > I'm wondering if the length of feature vector to be feed for SVM classifier necessarily with one column?
> > >
> > > Thanks in advance,
> > > Fernando
> > >
> > >
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