Improved spec

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403666

dlib/svm/linearly_independent_subset_finder_abstract.h

@@ -32,16 +32,15 @@ namespace dlib
                 in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel to 
                 decide which points are more linearly independent than others.  The metric is 
                 simply the squared distance between a test point and the subspace spanned by 
-                the current set of dictionary vectors.
+                the set of dictionary vectors.
 
                 Each time you present this object with a new sample point (via this->add()) 
                 it calculates the projection distance and if it is sufficiently large then this 
-                new point is included into the current dictionary.  Note that this object can
-                be configured to have a maximum size.  Once the max dictionary size is reached
-                each new point kicks out a previous point.  This is done by selecting the current
-                dictionary vector that has the smallest projection error onto the others.  That 
-                is, the "least linearly independent" vector is removed to make room for the 
-                new one.
+                new point is included into the dictionary.  Note that this object can be configured 
+                to have a maximum size.  Once the max dictionary size is reached each new point 
+                kicks out a previous point.  This is done by removing the dictionary vector that 
+                has the smallest projection distance onto the others.  That is, the "least linearly 
+                independent" vector is removed to make room for the new one.
         !*/
 
     public: