[mlpack-git] master: Propagate documentation fixes to trunk. (8b6b398)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40

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commit 8b6b398e1e22d21d109eded3bb96d3d3b6132758
Author: Ryan Curtin <ryan at ratml.org>
Date:   Mon Jul 28 14:32:06 2014 +0000

    Propagate documentation fixes to trunk.


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8b6b398e1e22d21d109eded3bb96d3d3b6132758
 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