Global refinement of random forest

@article{Ren2015GlobalRO,
  title={Global refinement of random forest},
  author={Shaoqing Ren and Xudong Cao and Yichen Wei and Jian Sun},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={723-730}
}
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 refinement and global pruning, to improve a pre-trained random forest. The proposed global refinement jointly relearns the leaf nodes of all trees under a global… CONTINUE READING
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