Jack,
sorry for the late answer.
I agree that my last post is misleading. Here a new try:
* *
Increasing the value of *C* (...) forces the creation of a more accurate
model, that may not generalise well.(Try to imagine the feature space with
the two mapped sets very far from each other ) A model t
Pau,
Sorry for getting back to you for this again. I am getting confused about
your interpretation of 3). It is obvious from your code that increasing C
results in* smaller *number of SVs, this seems to contradict with your
interpretation " * Increasing the value of C (...) forces the creation of
Hi Jack,
to 1) and 2) there are telling you the same. I recommend you to read the
first sections of the article it is very well writen and clear. There you
will read about duality.
to 3) I interpret the scatter plot so: * Increasing the value of C (...)
forces the creation of a more accurate mode
Pau,
Thanks a lot for your email, I found it very helpful. Please see below for
my reply, thanks.
-Jack
On Wed, Jul 14, 2010 at 10:36 AM, Pau Carrio Gaspar wrote:
> Hello Jack,
>
> 1 ) why do you thought that " larger C is prone to overfitting than smaller
> C" ?
>
*There is some statement
Hello Jack,
1 ) why do you thought that " larger C is prone to overfitting than smaller
C" ?
2 ) if you look at the formulation of the quadratic program problem you will
see that C rules the error of the "cutting plane " ( and overfitting ).
Therfore for hight C you allow that the "cutting plan
Hi,
I have a question about the parameter C (cost) in svm function in e1071. I
thought larger C is prone to overfitting than smaller C, and hence leads to
more support vectors. However, using the Wisconsin breast cancer example on
the link:
http://planatscher.net/svmtut/svmtut.html
I found that th
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