Boosted Binary Regression Trees (BBRT) is a powerful regression method proposed in . BBRT combines binary regression trees  using a gradient boosting technique.
There are several variants proposed in . In , it is assumed that the target is a scalar value. However, it is trivial to extend the method to vector target cases by proper modifications.
This code is based on "LS_Boost" described in  but it can also handle vector target cases. In other words, you do not need to train an independent regressor for each target dimension, unlike Support Vector Regression.
The detail of the algorithm this code implements can be found in .
 J. H. Friedman. Greedy Function Approximation: a Gradient Boosting Machine. Annals of Statistics, 2001.
 Kota Hara and Rama Chellappa, Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation, CVPR 2013.
 Breiman, Leo; Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. ISBN 978-0-412-04841-8.
Kota Hara (2023). Boosted Binary Regression Trees (https://www.mathworks.com/matlabcentral/fileexchange/42130-boosted-binary-regression-trees), MATLAB Central File Exchange. Retrieved .
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Added a sentence to README.txt
Added instruction with some sample data.
Faster training by mex for finding the best splitting function
Slightly changed the description. No changes to the code.