mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 14:29:49 +08:00
16 lines
1.2 KiB
ReStructuredText
16 lines
1.2 KiB
ReStructuredText
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.
|