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» Calibrating Random Forests
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SADM
2011
13 years 11 days 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 4 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
13 years 11 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 9 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 9 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