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» The Random Subspace Method for Constructing Decision Forests
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PAMI
1998
127views more  PAMI 1998»
9 years 6 months ago
The Random Subspace Method for Constructing Decision Forests
—Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is ...
Tin Kam Ho
IDA
2007
Springer
10 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
ICASSP
2008
IEEE
10 years 1 months ago
A weighted subspace approach for improving bagging performance
Bagging is an ensemble method that uses random resampling of a dataset to construct models. In classification scenarios, the random resampling procedure in bagging induces some c...
Qu-Tang Cai, Chun-Yi Peng, Chang-Shui Zhang
FGR
2004
IEEE
200views Biometrics» more  FGR 2004»
9 years 10 months ago
Using Random Subspace to Combine Multiple Features for Face Recognition
LDA is a popular subspace based face recognition approach. However, it often suffers from the small sample size problem. When dealing with the high dimensional face data, the LDA ...
Xiaogang Wang, Xiaoou Tang
ECAI
2006
Springer
9 years 10 months ago
Ensembles of Grafted Trees
Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method is used to complete the tree. In this work, the fi...
Juan José Rodríguez, Jesús Ma...
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