[R] SVM linear kernel and SV

[David Meyer]

Gladys,

> 
>  I've used  svm() with a linear kernel and I'd like to plot the linear
>  
> hyperplane and the support vectors. I use plot.svm() and, according to
> me, I would have found aligned support vectors (because the hyperplane
> is linear) for each class but it wasn't the case. Could you explain me
> why ?

In how far does the plot give you the impression is wouldn't? The two
classes look pretty separated to me.

> 
> In addition, when I change the option 'scale' (from TRUE to FALSE) the
> 
> results change. 

(Which results?) The plot is, of course, slightly different since the
model is based on different data, but the class predictions (on the
training data) are the same. Why does this surprise you?

Could you explain me why ? the option 'scale' of svm() 
> acts on the dataset or on the weight vector w and threshold b  ?

On the data set, and therefore also on w and b.

Best,
David


-- 
Dr. David Meyer
Department of Information Systems and Process Management

Vienna University of Economics and Business Administration
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