indeed SVM (libsvm / liblinear) could benefit also from a path strategy.

Alex

On Mon, Feb 11, 2013 at 8:39 AM, Wei LI <li...@ee.cuhk.edu.hk> wrote:
> In my point of view, to optimize the hyperparameters can not use standard
> optimization techniques(or else it will become a parameters and cannot be
> set empirically?) So some heuristic in brute force searching maybe a good
> idea. I am thinking another heuristic to accelerate such process: maybe a
> warm start after we have trained models. I do now have any sound theory
> about this, but for SVM in particular, as the global optimal is guaranteed,
> maybe a warm start will accelerate of the process to convergence without
> biasing the trained model?
>
> Best Regards,
> Wei LI
>
> On Mon, Feb 11, 2013 at 11:03 AM, <amuel...@ais.uni-bonn.de> wrote:
>>
>> I have a pull request for randomized seaech but I need to update it as it
>> is quite old...
>>
>>
>>
>> Ronnie Ghose <ronnie.gh...@gmail.com> schrieb:
>>>
>>> afaik yes. Please tell me if i'm wrong, more experienced scikitters :)
>>>
>>>
>>> On Sun, Feb 10, 2013 at 9:23 PM, Yaser Martinez
>>> <yaser.marti...@gmail.com> wrote:
>>>>
>>>> Any further development on this? Is a "brute force" grid search the only
>>>>  alternative to the problem of parameter selection for lets say SVMs?
>>>>
>>>>
>>>>
>>>>
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