{quote}
k = 1
Otherwise as in the Pegasos article. No parallelism.
{quote}
I confused. As the consequence, what is the motivation behind integrating
the Pegasos into Mahout.
Can you estimate that in which situation, this implementation can outperform
the original Pegasos? Large-scale data set or any other concern?
With this implementation, how can we take advantage of Map-reduce framework?
On Tue, Dec 22, 2009 at 12:44 PM, Ted Dunning (JIRA) <j...@apache.org>wrote:
>
> [
> https://issues.apache.org/jira/browse/MAHOUT-227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12793497#action_12793497]
>
> Ted Dunning commented on MAHOUT-227:
> ------------------------------------
>
> {quote}
> Can you specify this sequential implementation?
> {quote}
>
> k = 1
>
> Otherwise as in the Pegasos article.
>
>
> > Parallel SVM
> > ------------
> >
> > Key: MAHOUT-227
> > URL: https://issues.apache.org/jira/browse/MAHOUT-227
> > Project: Mahout
> > Issue Type: Task
> > Components: Classification
> > Reporter: zhao zhendong
> > Attachments: ParallelPegasos.doc, ParallelPegasos.pdf
> >
> >
> > I wrote a proposal of parallel algorithm for SVM training. Any comment is
> welcome.
>
> --
> This message is automatically generated by JIRA.
> -
> You can reply to this email to add a comment to the issue online.
>
>
--
-------------------------------------------------------------
Zhen-Dong Zhao (Maxim)
<><<><><><><><><><>><><><><><>>>>>>
Department of Computer Science
School of Computing
National University of Singapore
><><><><><><><><><><><><><><><><<<<
Homepage:http://zhaozhendong.googlepages.com
Mail: zhaozhend...@gmail.com
>>>>>>><><><><><><><><<><>><><<<<<<
