On Fri, Feb 17, 2012 at 3:53 PM, Andreas wrote:
> ASSET: Approximate Stochastic Subgradient
> Estimation Training for Support Vector Machines Sangkyun Lee and Stephen
> J. Wright
> Code available online but didn't try yet.
> I think I gave this reference before when we were talking about GSoC.
S
2012/2/17 Lars Buitinck :
> 2012/2/13 Olivier Grisel :
>> I don't know for the polynomial kernel part but since C is scale
>> according to the number of sample, C=1e4 or more is required for text
>> classification.
>
> I finally had time to try this out. You're absolutely right; C should
> be aroun
2012/2/13 Andreas :
> On 02/13/2012 09:49 PM, Lars Buitinck wrote:
>> I verified that the features coming from text.Vectorizer are
>> normalized; they're all in the range [-1, 1].
>>
> I guess that is not the problem here but chi2 is only defined for
> positive input, right?
Strictly, yes, so I sh
2012/2/13 Olivier Grisel :
> I don't know for the polynomial kernel part but since C is scale
> according to the number of sample, C=1e4 or more is required for text
> classification.
I finally had time to try this out. You're absolutely right; C should
be around 1e11 for a quadratic kernel SVM to
On 02/17/2012 01:35 PM, Olivier Grisel wrote:
> 2012/2/17 Andreas:
>
>>
>>> With regards to LaSVM, I would rather pitch a summer of code project as
>>> having a good on-line SVM solver, that would incorporate the core ideas
>>> from LaSVM, but that would also be useable in a real out-of-c
On 02/15/2012 02:04 AM, Ian Goodfellow wrote:
> 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
2012/2/17 Andreas :
>
>> With regards to LaSVM, I would rather pitch a summer of code project as
>> having a good on-line SVM solver, that would incorporate the core ideas
>> from LaSVM, but that would also be useable in a real out-of-core setting.
>> I believe that doing this right, including worr
> With regards to LaSVM, I would rather pitch a summer of code project as
> having a good on-line SVM solver, that would incorporate the core ideas
> from LaSVM, but that would also be useable in a real out-of-core setting.
> I believe that doing this right, including worrying about the parameter
