[mlpack] 232/324: Propagate documentation fixes to trunk.

Barak A. Pearlmutter barak+git at cs.nuim.ie
Sun Aug 17 08:22:13 UTC 2014


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bap pushed a commit to branch svn-trunk
in repository mlpack.

commit 1707b53bd60d13e824072d6cf4ca2241ec77f7dd
Author: rcurtin <rcurtin at 9d5b8971-822b-0410-80eb-d18c1038ef23>
Date:   Mon Jul 28 14:32:06 2014 +0000

    Propagate documentation fixes to trunk.
    
    
    git-svn-id: http://svn.cc.gatech.edu/fastlab/mlpack/trunk@16886 9d5b8971-822b-0410-80eb-d18c1038ef23
---
 doc/tutorials/emst/emst.txt | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/doc/tutorials/emst/emst.txt b/doc/tutorials/emst/emst.txt
index 44ecea6..20b1341 100644
--- a/doc/tutorials/emst/emst.txt
+++ b/doc/tutorials/emst/emst.txt
@@ -17,9 +17,10 @@ Among other applications, the EMST can be used to compute hierarchical clusterin
 of data.  A <em>single-linkage clustering</em> can be obtained from the EMST by deleting
 all edges longer than a given cluster length.  This technique is also referred to as a <em>Friends-of-Friends</em> clustering in the astronomy literature.
 
-MLPACK includes an implementation of <b>Dual-Tree Boruvka</b> on \f$kd\f$-trees,
-the empirically and theoretically fastest EMST algorithm.  For more details,
-see the following paper:
+MLPACK includes an implementation of <b>Dual-Tree Boruvka</b> which uses
+\f$kd\f$-trees by default; this is the empirically and theoretically fastest
+EMST algorithm.  In addition, the implementation supports the use of different
+trees via templates.  For more details, see the following paper:
 
 @code
 @inproceedings{march2010fast,
@@ -118,7 +119,10 @@ Note that it is also possible to compute the EMST using a naive (\f$O(N^2)\f$) a
 
 The 'DualTreeBoruvka' class contains our implementation of the Dual-Tree Boruvka algorithm.
 
-The class has two constructors: the first takes the data set, constructs the \f$kd\f$-tree, and computes the MST.  The second takes data set and an already constructed tree.
+The class has two constructors: the first takes the data set, constructs the
+tree (where the type of tree constructed is the TreeType template parameter),
+and computes the MST.  The second takes data set and an already constructed
+tree.
 
 The class provides one method that performs the MST computation:
 @code

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