On Thu, 9 Sep 2004 15:16:34 -0500, Monika Ray <[log in to unmask]> wrote:
> When one applies the SVM..one also changes other parameters such as
> lambda( or the conditioning parameter for quadratic programming) and
> epsilon(the width of the e-tube in the case of svm regression) and the
> different values necessary for a particular kernel to finally get the best
> model. So does one get the optimal and unique solution with each
> different value or is there a right value for each of these parameters
> after which one can be guaranteed the unique and optimal solution?
With the right value for each parameter you get the optimal solution
regarding this
parameter. For the optimal solution of the problem as a whole you need
all right parameters
and the right kernel. Fortunately for many problems a 'suboptimal'
solution is sufficient apart
from that with the limited precision of a computer you can't reach the
optimal solution anyway .)
> Regarding the microrray data- My opinion also matches yours. There are
> many papers out there that do gene expression analysis and though they
> always
> mention the fact that the number of samples is less than the number of
> features,
Maybe many features a highly correlated or are not important for the
problem, so an appropriate
feature selection can reduce the features needed.
Best wishes
Martin
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