From fd27ba248b0185efa7099e1b73a3be50c17f2513 Mon Sep 17 00:00:00 2001 From: Maria Dimashova Date: Tue, 9 Aug 2011 09:52:08 +0000 Subject: [PATCH] added doc on CvERTrees --- modules/ml/doc/ertrees.rst | 15 +++++++++++++++ modules/ml/doc/ml.rst | 1 + 2 files changed, 16 insertions(+) create mode 100644 modules/ml/doc/ertrees.rst diff --git a/modules/ml/doc/ertrees.rst b/modules/ml/doc/ertrees.rst new file mode 100644 index 0000000..c746b70 --- /dev/null +++ b/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. diff --git a/modules/ml/doc/ml.rst b/modules/ml/doc/ml.rst index cdf9a09..b83e7de 100644 --- a/modules/ml/doc/ml.rst +++ b/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 -- 2.7.4