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From build...@apache.org
Subject svn commit: r887375 - in /websites/staging/mahout/trunk/content: ./ users/classification/classifyingyourdata.html
Date Wed, 20 Nov 2013 16:50:33 GMT
Author: buildbot
Date: Wed Nov 20 16:50:32 2013
New Revision: 887375

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/classification/classifyingyourdata.html

Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Wed Nov 20 16:50:32 2013
@@ -1 +1 @@
-1543858
+1543870

Modified: websites/staging/mahout/trunk/content/users/classification/classifyingyourdata.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/classification/classifyingyourdata.html (original)
+++ websites/staging/mahout/trunk/content/users/classification/classifyingyourdata.html Wed
Nov 20 16:50:32 2013
@@ -381,39 +381,22 @@
 
   <div id="content-wrap" class="clearfix">
    <div id="main">
-    <p>+<em>Mahout_0.2</em>+</p>
-<p>After you've done the <a href="quickstart.html">Quickstart</a>
- and are familiar with the basics of Mahout, it is time to build a
-classifier from your own data. </p>
-<p>The following pieces <em>may</em> be useful for in getting started:</p>
+    <h1 id="classifying-data-from-the-command-line">Classifying data from the command
line</h1>
+<p>After you've done the <a href="../basics/quickstart.html">Quickstart</a>
and are familiar with the basics of Mahout, it is time to build a
+classifier from your own data. The following pieces <em>may</em> be useful for
in getting started:</p>
 <p><a name="ClassifyingYourData-Input"></a></p>
 <h1 id="input">Input</h1>
-<p>For starters, you will need your data in an appropriate Vector format
-(which has changed since Mahout 0.1)</p>
-<ul>
-<li>See <a href="creating-vectors.html">Creating Vectors</a></li>
-</ul>
-<p><a name="ClassifyingYourData-TextPreparation"></a></p>
-<h2 id="text-preparation">Text Preparation</h2>
-<ul>
-<li>See <a href="creating-vectors-from-text.html">Creating Vectors from Text</a>
-*
-http://www.lucidimagination.com/search/document/4a0e528982b2dac3/document_clustering</li>
-</ul>
+<p>For starters, you will need your data in an appropriate Vector format: See <a
href="../basics/creating-vectors.html">Creating Vectors</a> as well as <a href="../basics/creating-vectors-from-text.html">Creating
Vectors from Text</a>.</p>
 <p><a name="ClassifyingYourData-RunningtheProcess"></a></p>
 <h1 id="running-the-process">Running the Process</h1>
-<p><a name="ClassifyingYourData-NaiveBayes"></a></p>
-<h2 id="naive-bayes">Naive Bayes</h2>
-<p>Background: <a href="-bayesian-.html">Naive Bayes Classification </a></p>
-<p>Documentation of running naive bayes from the command line: <a href="bayesian-commandline.html">bayesian-commandline</a></p>
-<p><a name="ClassifyingYourData-C-Bayes"></a></p>
-<h2 id="c-bayes">C-Bayes</h2>
-<p>Background: <a href="-https://issues.apache.org/jira/browse/mahout-60-.html">C-Bayes
Classification </a></p>
-<p>Documentation of running c-bayes from the command line: <a href="c-bayes-commandline.html">c-bayes-commandline</a></p>
-<p><a name="ClassifyingYourData-RandomForests"></a></p>
-<h2 id="random-forests">Random Forests</h2>
-<p>Background: <a href="-http://cwiki.apache.org/mahout/random-forests.html-.html">Random
Forests Classification </a></p>
-<p>Documentation of running random forests from the command line: <a href="breiman-example.html">Breiman
Example</a></p>
+<ul>
+<li>Logistic regression <a href="logistic-regression.html">background</a></li>
+<li><a href="naivebayes.html">Naive Bayes background</a> and <a href="bayesian-commandline.html">commandline</a>
options.</li>
+<li><a href="complementary-naive-bayes.html">Complementary naive bayes background</a>,
<a href="https://issues.apache.org/jira/browse/mahout-60.html">design</a>, and
<a href="c-bayes-commandline.html">c-bayes-commandline</a></li>
+<li><a href="https://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests">Random
Forests Classification</a> comes with a <a href="breiman-example.html">Breiman
example</a>. There is some really great documentation
+over at <a href="http://www.markhneedham.com/blog/2012/10/27/kaggle-digit-recognizer-mahout-random-forest-attempt/">Mark
Needham's blog</a>. Also checkout the description on <a href="http://shawnwan.wordpress.com/2012/06/01/mahout-0-7-random-forest-examples/">Xiaomeng
Shawn Wan
+s</a> blog.</li>
+</ul>
    </div>
   </div>     
 </div> 



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