Author: buildbot
Date: Sat Apr 11 23:30:45 2015
New Revision: 947232
Log:
Staging update by buildbot for mahout
Modified:
websites/staging/mahout/trunk/content/ (props changed)
websites/staging/mahout/trunk/content/index.html
Propchange: websites/staging/mahout/trunk/content/
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--- cms:source-revision (original)
+++ cms:source-revision Sat Apr 11 23:30:45 2015
@@ -1 +1 @@
-1672943
+1672944
Modified: websites/staging/mahout/trunk/content/index.html
==============================================================================
--- websites/staging/mahout/trunk/content/index.html (original)
+++ websites/staging/mahout/trunk/content/index.html Sat Apr 11 23:30:45 2015
@@ -284,12 +284,12 @@
<li>Naive Bayes Classification</li>
</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>
- <p><strong>11 Apr 2015 - Apache Mahout's next generation version 0.1.10
released</strong></h5>
- <p><strong>Apache Mahout would like to introduce a new math</strong> <a
href="http://mahout.apache.org/users/sparkbindings/home.html";><strong>environment
we call Samsara</strong></a>; 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. Itâs written in Scala with Mahout-specific
extensions, and runs most fully on Spark.</p></p>
-<p><p><a
href="http://mahout.apache.org/users/basics/algorithms.html";><strong>Mahout
Algorithms</strong></a> 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. Youâll find robust matrix decomposition algorithms as well as
a Naive Bayes classifier and collaborative filtering.</p></p>
+ <p>The three major components of Mahout are an environment for building
scalable algorithms, many new Scala + Spark (H2O in progress) algorithms, and
Mahout's mature Hadoop MapReduce algorithms.</p>
+ <h5><strong>11 Apr 2015 - Apache Mahout's next generation version 0.10.0
released</strong></h5>
+ <p><strong>Apache Mahout would like to introduce a new math</strong> <a
href="http://mahout.apache.org/users/sparkbindings/home.html";><strong>environment
we call Samsara</strong></a>, for its theme 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. Itâs written in Scala with Mahout-specific
extensions, and runs most fully on Spark.</p></p>
+<p><p><a
href="http://mahout.apache.org/users/basics/algorithms.html";><strong>Mahout
Algorithms</strong></a> include many new implementations built for speed on
Mahout-Samsara. They run on Spark and some on H2o, which means as much as a 10x
speed increase. Youâll find robust matrix decomposition algorithms as well as
a Naive Bayes classifier and collaborative filtering.</p></p>
<p><p><strong>Mahout MapReduce</strong> includes the best of Hadoop MapReduce
algorithms from Mahout v 0.9 but now with dependency updates and full Hadoop 2
support.</p></p>
-<p><p>With scalable we mean:</p>
+<p><h5>By scalable we mean:</h5>
<p><strong>Scalable to large data sets</strong>. Our <a
href="http://mahout.apache.org/users/basics/algorithms.html";>core
algorithms</a> for clustering, classfication and collaborative filtering are
implemented on top of scalable, distributed systems. However, contributions
that run on a single machine are welcome as well.</p>
<p><strong>Scalable to support your business case</strong>. Mahout is
distributed under a commercially friendly Apache Software
license.</p>
