Online Random Forests
A new and relatively faster implementation of this algorithm exists in my Online Multi-Class LPBoost package.
This package implements the “Online Random Forests” (ORF) algorithm of Saffari et al., ICCV-OLCV 2009 . This algorithm extends the offline Random Forests (RF) to learn from online training data samples. ORF is a multi-class classifier which is able to learn the classifier without 1-vs-all or 1-vs-1 binary decompositions.
ORF package is implemented in C++ and uses ATLAS/LAPACK subroutines for high performance computations. Currently, it is only tested under Linux (Debian and Ubuntu), but overall it should be possible to run the package on other operating systems with minimal modifications. For installation instructions refer to the INSTALL file in the package. Also the usage instructions are available in 0;README file.
- Download: OnlineForest-0.11.tar.gz (Release date: 03/10/2009)
- Repository: https://github.com/amirsaffari/online-random-forests
 Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, and Horst Bischof, ‘On-line Random Forests’ in 3rd IEEE ICCV Workshop on On-line Computer Vision, 2009.