svn commit: r1544145 - /mahout/site/mahout_cms/trunk/content/users/stuff/hidden-markov-models.mdtext

[is...@apache.org]

Author: isabel
Date: Thu Nov 21 11:58:47 2013
New Revision: 1544145

URL: http://svn.apache.org/r1544145
Log:
MAHOUT-1245 - formatting

Modified:
    mahout/site/mahout_cms/trunk/content/users/stuff/hidden-markov-models.mdtext

Modified: mahout/site/mahout_cms/trunk/content/users/stuff/hidden-markov-models.mdtext
URL: http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/stuff/hidden-markov-models.mdtext?rev=1544145&r1=1544144&r2=1544145&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/stuff/hidden-markov-models.mdtext (original)
+++ mahout/site/mahout_cms/trunk/content/users/stuff/hidden-markov-models.mdtext Thu Nov 21 11:58:47 2013
@@ -1,13 +1,16 @@
 Title: Hidden Markov Models
+
+# Hidden Markov Models
+
 <a name="HiddenMarkovModels-IntroductionandUsage"></a>
-### Introduction and Usage
+## Introduction and Usage
 
 Hidden Markov Models are used in multiple areas of Machine Learning, such
 as speech recognition, handwritten letter recognition or natural language
 processing. 
 
 <a name="HiddenMarkovModels-FormalDefinition"></a>
-### Formal Definition
+## Formal Definition
 
 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
@@ -28,26 +31,26 @@ containing the observation probabilities
 P(O=o_i|Y=y_j).
 
 <a name="HiddenMarkovModels-Problems"></a>
-### Problems
+## Problems
 
 Rabiner \[1\](1\.html)
  defined three main problems for HMM models:
 
-   1. Evaluation: Given a sequence O of observations and a model M, what is
+1. Evaluation: Given a sequence O of observations and a model M, what is
 the probability P(O|M) that sequence O was generated by model M. The
 Evaluation problem can be efficiently solved using the Forward algorithm
-   2. Decoding: Given a sequence O of observations and a model M, what is
+2. Decoding: Given a sequence O of observations and a model M, what is
 the most likely sequence Y*=argmax(Y) P(O|M,Y) of hidden variables to
 generate this sequence. The Decoding problem can be efficiently solved
 using the Viterbi algorithm.
-   3. Learning: Given a sequence O of observations, what is the most likely
+3. Learning: Given a sequence O of observations, what is the most likely
 model M*=argmax(M)P(O|M) to generate this sequence. The Learning problem
 can be efficiently solved using the Baum-Welch algorithm.
 
 <a name="HiddenMarkovModels-Resources"></a>
-### Resources
+## Resources
 
-\[1\](1\.html)
+\[1\]
  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.