Global refinement of random forest

IEEE Conference on Computer Vision and Pattern Recognition, pp. 723-730, 2015.

Cited by: 95|Views259
EI WOS

Abstract:

Random forest is well known as one of the best learning methods. In spite of its great success, it also has certain drawbacks: the heuristic learning rule does not effectively minimize the global training loss; the model size is usually too large for many real applications. To address the issues, we propose two techniques, global refineme...More

Code:

Data:

Your rating :
0

 

Tags
Comments