Global refinement of random forest
IEEE Conference on Computer Vision and Pattern Recognition, pp. 723-730, 2015.
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
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