> SVM has been known to have a unique and optimal solution...does this mean
> with each new C (and other parameter values ) value you get a unique and
> optimal solution? or is it
> for the right C/parameters value you will get the unique and optimal
> solution?
SVM has global optimal solution(s) because its training procedure is
essentially a quadratic problem (with linear constraints).
This nature does not change for any given C values.
In some cases ( due to the special form of kernel matrix), SVM may not have
a "unique" optimal solution.
> Also, its well known that microarray data have high number of features but
> not many samples. I have data where each class was repeated 3 times.
> That is -
>
> For Class 1 - 3 independent experiments (out of which 1 expt. was too
> noisy and had to be thrown out..so you end up with just 2 experimenst of
> valid results)
>
> For Class 2 - 3 independent experiments (out of which 1 expt. was too
> noisy and had to be thrown out..so you end up with just 2 experimenst of
> valid results)
>
> For class 3 - 3 independent experiments (all valid)
>
> and so on for 2 more classes - therefore have 5 classes all in all.
>
> Now My question is -
>
> I don't think there are enough examples per class. So is it wise to train
> an SVM on this?
Although you still can apply SVM to this dataset and get something out of
it,
I am not sure your result will be reliable and/or reasonable.
In fact, I don't know if it is possible for you to get *any* reliable
results out of this kind of dataset.
I really want to learn more about others' opinions in this kind situation.
jm
-------------------------------------------
Junshui Ma, Ph. D.
614-688-4893 (office)
Room 350A, Ohio Supercomputer Center
1224 Kinnear Rd., Columbus, OH 43212
http://www.osc.edu/~junshui
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