On Wed, Feb 15, 2012 at 8:26 AM, Olivier Grisel <[email protected]>wrote:
> 2012/2/15 Ian Goodfellow <[email protected]>:
> > Further update: I talked to Adam Coates and his code doesn't implement
> > a standard SVM. Instead it's an "L2 SVM" which squares all the slack
> > variables. So this probably explains the difference in performance I
> > observed prior to building this test case.
>
> Good to know. AFAIK this is the same loss (squared hinge) as used by
> default in liblinear. You could try to compare the outcome with
> LinearSVC (albeit you will get the memory copy of you dense input) or
> SGDClassifier (the penalty and loss parameters will allow you to
> adjust the objective function to your will).
>
... and still those (liblinear vs Adam Coates) are not exactly comparable
since
the bias term is penalized in liblinear while in Coates's I suppose it's
not.
Paolo
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