From 9b51f8dc00e7b722ea201ba36c8ed8cc53ffcad9 Mon Sep 17 00:00:00 2001
From: Davis King <davis@dlib.net>
Date: Sun, 16 May 2010 18:54:18 +0000
Subject: [PATCH] updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403634
---
 docs/docs/ml.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/ml.xml b/docs/docs/ml.xml
index 0552dd69f..b811b5291 100644
--- a/docs/docs/ml.xml
+++ b/docs/docs/ml.xml
@@ -560,7 +560,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                 In the above setting, all the training data consists of labeled samples.  
                 However, it would be nice to be able to benefit from unlabeled data.  
                 The idea of manifold regularization is to extract useful information from 
-                unlabeled data by defining which data samples are "close" to each other 
+                unlabeled data by first defining which data samples are "close" to each other 
                 (perhaps by using their 3 <a href="#find_k_nearest_neighbors">nearest neighbors</a>) 
                 and then adding a term to 
                 the loss function that penalizes any decision rule which produces