svn commit: r887520 - in /websites/staging/mahout/trunk/content: ./ users/stuff/hidden-markov-models.html

[bu...@apache.org]

Author: buildbot
Date: Thu Nov 21 11:58:51 2013
New Revision: 887520

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/stuff/hidden-markov-models.html

Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Thu Nov 21 11:58:51 2013
@@ -1 +1 @@
-1544143
+1544145

Modified: websites/staging/mahout/trunk/content/users/stuff/hidden-markov-models.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/stuff/hidden-markov-models.html (original)
+++ websites/staging/mahout/trunk/content/users/stuff/hidden-markov-models.html Thu Nov 21 11:58:51 2013
@@ -357,13 +357,14 @@
 
   <div id="content-wrap" class="clearfix">
    <div id="main">
-    <p><a name="HiddenMarkovModels-IntroductionandUsage"></a></p>
-<h3 id="introduction-and-usage">Introduction and Usage</h3>
+    <h1 id="hidden-markov-models">Hidden Markov Models</h1>
+<p><a name="HiddenMarkovModels-IntroductionandUsage"></a></p>
+<h2 id="introduction-and-usage">Introduction and Usage</h2>
 <p>Hidden Markov Models are used in multiple areas of Machine Learning, such
 as speech recognition, handwritten letter recognition or natural language
 processing. </p>
 <p><a name="HiddenMarkovModels-FormalDefinition"></a></p>
-<h3 id="formal-definition">Formal Definition</h3>
+<h2 id="formal-definition">Formal Definition</h2>
 <p>A Hidden Markov Model (HMM) is a statistical model of a process consisting
 of two (in our case discrete) random variables O and Y, which change their
 state sequentially. The variable Y with states {y_1, ... , y_n} is called
@@ -381,7 +382,7 @@ current state of Y.</p>
 containing the observation probabilities such that B[i,j]=
 P(O=o_i|Y=y_j).</p>
 <p><a name="HiddenMarkovModels-Problems"></a></p>
-<h3 id="problems">Problems</h3>
+<h2 id="problems">Problems</h2>
 <p>Rabiner [1](1.html)
  defined three main problems for HMM models:</p>
 <ol>
@@ -397,8 +398,8 @@ model M*=argmax(M)P(O|M) to generate thi
 can be efficiently solved using the Baum-Welch algorithm.</li>
 </ol>
 <p><a name="HiddenMarkovModels-Resources"></a></p>
-<h3 id="resources">Resources</h3>
-<p>[1](1.html)
+<h2 id="resources">Resources</h2>
+<p>[1]
  Lawrence R. Rabiner (February 1989). "A tutorial on Hidden Markov Models
 and selected applications in speech recognition". Proceedings of the IEEE
 77 (2): 257-286. doi:10.1109/5.18626.</p>