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» Calibrating Random Forests
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SADM
2011
13 years 2 months ago
Random survival forests for high-dimensional data
: Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problem...
Hemant Ishwaran, Udaya B. Kogalur, Xi Chen, Andy J...

Book
34534views
15 years 6 months ago
OpenCV - Open Source Computer Vision Reference Manual
OpenCV is a C/C++ computer vision library originally developed by Intel. It is free for commercial and research use under a BSD license. The library is cross-platform. It is highl...
Intel
IDA
2007
Springer
14 years 1 months ago
Combining Bagging and Random Subspaces to Create Better Ensembles
Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in b...
Pance Panov, Saso Dzeroski
IBPRIA
2007
Springer
13 years 11 months ago
Random Forest for Gene Expression Based Cancer Classification: Overlooked Issues
Random forest is a collection (ensemble) of decision trees. It is a popular ensemble technique in pattern recognition. In this article, we apply random forest for cancer classifica...
Oleg Okun, Helen Priisalu
RANDOM
1999
Springer
13 years 11 months ago
A Randomized Time-Work Optimal Parallel Algorithm for Finding a Minimum Spanning Forest
We present a randomized algorithm to nd a minimum spanning forest (MSF) in an undirected graph. With high probability, the algorithm runs in logarithmic time and linear work on an...
Seth Pettie, Vijaya Ramachandran