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From Xiangrui Meng <men...@gmail.com>
Subject Re: Spark LIBLINEAR
Date Mon, 12 May 2014 14:53:22 GMT
Hi Chieh-Yen,

Great to see the Spark implementation of LIBLINEAR! We will definitely
consider adding a wrapper in MLlib to support it. Is the source code
on github?

Deb, Spark LIBLINEAR uses BSD license, which is compatible with Apache.

Best,
Xiangrui

On Sun, May 11, 2014 at 10:29 AM, Debasish Das <debasish.das83@gmail.com> wrote:
> Hello Prof. Lin,
>
> Awesome news ! I am curious if you have any benchmarks comparing C++ MPI
> with Scala Spark liblinear implementations...
>
> Is Spark Liblinear apache licensed or there are any specific restrictions on
> using it ?
>
> Except using native blas libraries (which each user has to manage by pulling
> in their best proprietary BLAS package), all Spark code is Apache licensed.
>
> Thanks.
> Deb
>
>
> On Sun, May 11, 2014 at 3:01 AM, DB Tsai <dbtsai@stanford.edu> wrote:
>>
>> Dear Prof. Lin,
>>
>> Interesting! We had an implementation of L-BFGS in Spark and already
>> merged in the upstream now.
>>
>> We read your paper comparing TRON and OWL-QN for logistic regression with
>> L1 (http://www.csie.ntu.edu.tw/~cjlin/papers/l1.pdf), but it seems that it's
>> not in the distributed setup.
>>
>> Will be very interesting to know the L2 logistic regression benchmark
>> result in Spark with your TRON optimizer and the L-BFGS optimizer against
>> different datasets (sparse, dense, and wide, etc).
>>
>> I'll try your TRON out soon.
>>
>>
>> Sincerely,
>>
>> DB Tsai
>> -------------------------------------------------------
>> My Blog: https://www.dbtsai.com
>> LinkedIn: https://www.linkedin.com/in/dbtsai
>>
>>
>> On Sun, May 11, 2014 at 1:49 AM, Chieh-Yen <r01944006@csie.ntu.edu.tw>
>> wrote:
>>>
>>> Dear all,
>>>
>>> Recently we released a distributed extension of LIBLINEAR at
>>>
>>> http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/
>>>
>>> Currently, TRON for logistic regression and L2-loss SVM is supported.
>>> We provided both MPI and Spark implementations.
>>> This is very preliminary so your comments are very welcome.
>>>
>>> Thanks,
>>> Chieh-Yen
>>
>>
>

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