Hi Leon
Yes, it is open access, We will put it in one of the authors, zhilin Zhang's
homepage. I will announce once it is available .
It is of great help if you could test it! I will learn git first and fork
sklearn to my home folder.
Best wishes !
Liu benyuan
在 2012-11-28，15:11，Leon Palafox <[email protected]> 写道：
> Hey Liu.
>
> You can probably fork it in github and submit your code as a module in
> sklearn, then you would have a healthy test period to finally put it in the
> latest release.
>
> If you have it on open access, I would be happy to test it and help you with
> the methods, specially because most of the methods like fit and train have to
> be consistent.
>
> Regards
>
> Leon
>
>
> On Wed, Nov 28, 2012 at 4:08 PM, <[email protected]> wrote:
>> Dear scikit-learn community:
>>
>> Block Sparse Bayesian Learning is a powerful CS algorithm for recovering
>> block sparse signals with structures, and shows the additional benefits of
>> reconstruct non-sparse signals, see Dr. zhilin zhang's websites:
>> http://dsp.ucsd.edu/~zhilin/BSBL.html
>>
>> I currently implement the BSBL-BO algorithm by Zhang and a fast version of
>> BSBL algorithm recently proposed by us, called BSBL-FM, in python. Plus many
>> demos using these two codes. Does scikit-learn community welcome such type
>> of code ? what is the procedure to submit the code in the mainstream of
>> scikit learn?
>>
>> Thanks for the great project!
>>
>> Liu benyuan
>>
>> ------------------------------------------------------------------------------
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>
>
>
> --
> Leon Palafox, M.Sc
> PhD Candidate
> Iba Laboratory
> +81-3-5841-8436
> University of Tokyo
> Tokyo, Japan.
>
>
> ------------------------------------------------------------------------------
> Keep yourself connected to Go Parallel:
> INSIGHTS What's next for parallel hardware, programming and related areas?
> Interviews and blogs by thought leaders keep you ahead of the curve.
> http://goparallel.sourceforge.net
> _______________________________________________
> Scikit-learn-general mailing list
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> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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INSIGHTS What's next for parallel hardware, programming and related areas?
Interviews and blogs by thought leaders keep you ahead of the curve.
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