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From Xiangrui Meng <men...@gmail.com>
Subject Re: How to process multiple classification with SVM in MLlib
Date Sun, 08 Jun 2014 04:07:23 GMT
At this time, you need to do one-vs-all manually for multiclass
training. For your second question, if the algorithm is implemented in
Java/Scala/Python and designed for single machine, you can broadcast
the dataset to each worker, train models on workers. If the algorithm
is implemented in a different language, maybe you need pipe to train
the models outside JVM (similar to Hadoop Streaming). If the algorithm
is designed for a different parallel platform, then it may be hard to
use it in Spark. -Xiangrui

On Sat, Jun 7, 2014 at 7:15 AM, littlebird <cxp526@163.com> wrote:
> Hi All,
>   As we know, In MLlib the SVM is used for binary classification. I wonder
> how to train SVM model for mutiple classification in MLlib. In addition, how
> to apply the machine learning algorithm in Spark if the algorithm isn't
> included in MLlib. Thank you.
>
>
>
> --
> View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/How-to-process-multiple-classification-with-SVM-in-MLlib-tp7174.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.

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