svn commit: r944372 - in /websites/staging/mahout/trunk/content: ./ users/classification/bayesian.html

[bu...@apache.org]

Author: buildbot
Date: Thu Mar 19 19:32:51 2015
New Revision: 944372

Log:
Staging update by buildbot for mahout

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

Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Thu Mar 19 19:32:51 2015
@@ -1 +1 @@
-1665101
+1667854

Modified: websites/staging/mahout/trunk/content/users/classification/bayesian.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/classification/bayesian.html (original)
+++ websites/staging/mahout/trunk/content/users/classification/bayesian.html Thu Mar 19 19:32:51 2015
@@ -246,18 +246,18 @@
   <div id="content-wrap" class="clearfix">
    <div id="main">
     <h1 id="naive-bayes">Naive Bayes</h1>
-<h3 id="intro">Intro</h3>
+<h2 id="intro">Intro</h2>
 <p>Mahout currently has two Naive Bayes implementations.  The first is standard Multinomial Naive Bayes. The second is an implementation of Transformed Weight-normalized Complement Naive Bayes as introduced by Rennie et al. <a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf">[1]</a>. We refer to the former as Bayes and the latter as CBayes.</p>
 <p>Where Bayes has long been a standard in text classification, CBayes is an extension of Bayes that performs particularly well on datasets with skewed classes and has been shown to be competitive with algorithms of higher complexity such as Support Vector Machines. </p>
-<h3 id="implementations">Implementations</h3>
+<h2 id="implementations">Implementations</h2>
 <p>Both Bayes and CBayes are currently trained via MapReduce Jobs. Testing and classification can be done via a MapReduce Job or sequentially.  Mahout provides CLI drivers for preprocessing, training and testing. A Spark implementation is currently in the works (<a href="https://issues.apache.org/jira/browse/MAHOUT-1493">MAHOUT-1493</a>).</p>
-<h3 id="preprocessing-and-algorithm">Preprocessing and Algorithm</h3>
+<h2 id="preprocessing-and-algorithm">Preprocessing and Algorithm</h2>
 <p>As described in <a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf">[1]</a> Mahout Naive Bayes is broken down into the following steps (assignments are over all possible index values):  </p>
 <ul>
 <li>Let <code>\(\vec{d}=(\vec{d_1},...,\vec{d_n})\)</code> be a set of documents; <code>\(d_{ij}\)</code> is the count of word <code>\(i\)</code> in document <code>\(j\)</code>.</li>
-<li>Let <code>\(\vec{y}=(\vec{y_1},...,\vec{y_n})\)</code> be their labels.</li>
+<li>Let <code>\(\vec{y}=(y_1,...,y_n)\)</code> be their labels.</li>
 <li>Let <code>\(\alpha_i\)</code> be a smoothing parameter for all words in the vocabulary; let <code>\(\alpha=\sum_i{\alpha_i}\)</code>. </li>
-<li><strong>Preprocessing</strong>: TF-IDF transformation and L2 length normalization of <code>\(\vec{d}\)</code><ol>
+<li><strong>Preprocessing</strong>(via seq2Sparse) TF-IDF transformation and L2 length normalization of <code>\(\vec{d}\)</code><ol>
 <li><code>\(d_{ij} = \sqrt{d_{ij}}\)</code> </li>
 <li><code>\(d_{ij} = d_{ij}\left(\log{\frac{\sum_k1}{\sum_k\delta_{ik}+1}}+1\right)\)</code> </li>
 <li><code>\(d_{ij} =\frac{d_{ij}}{\sqrt{\sum_k{d_{kj}^2}}}\)</code> </li>
@@ -281,7 +281,7 @@
 </li>
 </ul>
 <p>As we can see, the main difference between Bayes and CBayes is the weight calculation step.  Where Bayes weighs terms more heavily based on the likelihood that they belong to class <code>\(c\)</code>, CBayes seeks to maximize term weights on the likelihood that they do not belong to any other class.  </p>
-<h3 id="running-from-the-command-line">Running from the command line</h3>
+<h2 id="running-from-the-command-line">Running from the command line</h2>
 <p>Mahout provides CLI drivers for all above steps.  Here we will give a simple overview of Mahout CLI commands used to preprocess the data, train the model and assign labels to the training set. An <a href="https://github.com/apache/mahout/blob/master/examples/bin/classify-20newsgroups.sh">example script</a> is given for the full process from data acquisition through classification of the classic <a href="https://mahout.apache.org/users/classification/twenty-newsgroups.html">20 Newsgroups corpus</a>.  </p>
 <ul>
 <li>
@@ -327,7 +327,7 @@ Classification and testing on a holdout
 
 </li>
 </ul>
-<h3 id="command-line-options">Command line options</h3>
+<h2 id="command-line-options">Command line options</h2>
 <ul>
 <li><strong>Preprocessing:</strong></li>
 </ul>
@@ -393,12 +393,12 @@ Classification and testing on a holdout
 
 </li>
 </ul>
-<h3 id="examples">Examples</h3>
+<h2 id="examples">Examples</h2>
 <p>Mahout provides an example for Naive Bayes classification:</p>
 <ol>
 <li><a href="twenty-newsgroups.html">Classify 20 Newsgroups</a></li>
 </ol>
-<h3 id="references">References</h3>
+<h2 id="references">References</h2>
 <p>[1]: Jason D. M. Rennie, Lawerence Shih, Jamie Teevan, David Karger (2003). <a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf">Tackling the Poor Assumptions of Naive Bayes Text Classifiers</a>. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003).</p>
    </div>
   </div>