added doc on CvERTrees

modules/ml/doc/ertrees.rst

@@ -0,0 +1,15 @@
+Extremely randomized trees
+==========================
+
+Extremely randomized trees have been introduced by Pierre Geurts, Damien Ernst and Louis Wehenkel in the article "Extremely randomized trees", 2006 [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.7485&rep=rep1&type=pdf]. The algorithm of growing Extremely randomized trees is similar to :ref:`Random Trees` (Random Forest), but there are two differences:
+
+#. Extremely randomized trees don't apply the bagging procedure to constract the training samples for each tree. The same input training set is used to train all trees.
+
+#. Extremely randomized trees pick a node split very extremely (both a variable index and variable spliting value are chosen randomly), whereas Random Forest finds the best split (optimal one by variable index and variable spliting value) among random subset of variables.
+
+
+CvERTrees
+--------
+.. ocv:class:: CvERTrees
+
+    The class implements the Extremely randomized trees algorithm. ``CvERTrees`` is inherited from :ocv:class:`CvRTrees` and has the same interface, so see description of :ocv:class:`CvRTrees` class to get detailes. To set the training parameters of Extremely randomized trees the same class :ocv:class:`CvRTParams` is used.

modules/ml/doc/ml.rst

@@ -17,6 +17,7 @@ Most of the classification and regression algorithms are implemented as C++ clas
     boosting
     gradient_boosted_trees
     random_trees
+    ertrees
     expectation_maximization
     neural_networks
     mldata