There is a cool new version of Lasvm in vowpal wabbit that I want to 
implement, but probably not inside sklearn as it is too fresh.


On 12/15/2014 10:32 AM, Sebastian Raschka wrote:
> Yes, unfortunately that's the nature of SVM. I  However, there have been 
> implementations with on-line learning capabilities for SVM that are (promised 
> to be) nearly as accurate as the batch learning variant. Maybe the 
> partial_fit could be a useful task  for the GitHub "issue" list. Here would 
> be some implementations of online learning SVMs that could be used for 
> inspiration:
>
> http://leon.bottou.org/projects/lasvm 
> http://www.isn.ucsd.edu/svm/incremental/
> http://www.cpdiehl.org/code.html
> http://people.eng.unimelb.edu.au/shiltona/svm/
>
> Best,
> Sebastian
>
>> On Dec 15, 2014, at 9:52 AM, Andy <t3k...@gmail.com> wrote:
>>
>> On 12/14/2014 11:03 PM, Joel Nothman wrote:
>>> If the estimator supports `partial_fit`, you can use that, repeatedly,
>>> instead of `fit`.
>>>
>>> See documentation:
>>> http://scikit-learn.org/stable/modules/scaling_strategies.html
>>> http://scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_patches.html
>>>
>> If the classifier does not have "partial_fit", as is the case for the
>> SVC, then there is no way
>> apart from refitting on the whole data, sorry.
>>
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