X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=folded-ctf.git;a=blobdiff_plain;f=decision_tree.h;h=5adcb0f3e25e33e569b41d248694d1a517e91635;hp=59dba57c7b3e4a28c4d8c3540f60312547d32ff7;hb=aed34255065b18c445d096f51bd2091833810a81;hpb=d922ad61d35e9a6996730bec24b16f8bf7bc426c

diff --git a/decision_tree.h b/decision_tree.h
index 59dba57..5adcb0f 100644
--- a/decision_tree.h
+++ b/decision_tree.h
@@ -12,10 +12,22 @@
 // You should have received a copy of the GNU General Public License     //
 // along with this program. If not, see <http://www.gnu.org/licenses/>.  //
 //                                                                       //
-// Written by Francois Fleuret, (C) IDIAP                                //
+// Written by Francois Fleuret                                           //
+// (C) Idiap Research Institute                                          //
+//                                                                       //
 // Contact <francois.fleuret@idiap.ch> for comments & bug reports        //
 ///////////////////////////////////////////////////////////////////////////
 
+/*
+
+  An implementation of the classifier with a decision tree. Each node
+  simply thresholds one of the component, and is chosen for maximum
+  loss reduction locally during training. The leaves are labelled with
+  the classifier response, which is chosen again for maximum loss
+  reduction.
+
+ */
+
 #ifndef DECISION_TREE_H
 #define DECISION_TREE_H
 
@@ -26,14 +38,14 @@
 
 class DecisionTree : public Classifier {
 
+  static const int min_nb_samples_for_split = 5;
+
   int _feature_index;
   scalar_t _threshold;
   scalar_t _weight;
 
   DecisionTree *_subtree_lesser, *_subtree_greater;
 
-  static const int min_nb_samples_for_split = 5;
-
   void pick_best_split(SampleSet *sample_set,
                        scalar_t *loss_derivatives);