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Posted to commits@mahout.apache.org by ap...@apache.org on 2015/04/12 00:53:39 UTC

svn commit: r1672942 - /mahout/site/mahout_cms/trunk/content/index.mdtext

Author: apalumbo
Date: Sat Apr 11 22:53:38 2015
New Revision: 1672942

URL: http://svn.apache.org/r1672942
Log:
CMS commit to mahout by apalumbo

Modified:
    mahout/site/mahout_cms/trunk/content/index.mdtext

Modified: mahout/site/mahout_cms/trunk/content/index.mdtext
URL: http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/index.mdtext?rev=1672942&r1=1672941&r2=1672942&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/index.mdtext (original)
+++ mahout/site/mahout_cms/trunk/content/index.mdtext Sat Apr 11 22:53:38 2015
@@ -26,7 +26,7 @@
     </ul>
   </div>
   <p>The three major components of Mahout are an environment for building scalable algorithms, many new Scala + Spark (H2O in progress) algorithms, and Mahouts mature Hadoop MapReduce algorithms.</p>
-  <h5> **11 Apr 2015 - Apache Mahout's next generation version 0.1.10 released**</h5>
+  <p><h5> **11 Apr 2015 - Apache Mahout's next generation version 0.1.10 released**</h5></p>
   <p>**Apache Mahout would like to introduce a new math** [**environment we call Samsara**](http://mahout.apache.org/users/sparkbindings/home.html); for its symbol of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its base are general linear algebra and statistical operations along with the data structures to support them. It’s written in Scala with Mahout-specific extensions, and runs most fully on Spark.</p>
 
   <p>[**Mahout Algorithms**](http://mahout.apache.org/users/basics/algorithms.html) include many new implementations built for speed on Mahout-Samsara. They run on Spark and some on H2o, which means as much as 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering.</p>