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PAMI
1998
127views more  PAMI 1998»
13 years 5 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
JCIT
2010
190views more  JCIT 2010»
13 years 3 days ago
Application of Feature Extraction Method in Customer Churn Prediction Based on Random Forest and Transduction
With the development of telecom business, customer churn prediction becomes more and more important. An outstanding issue in customer churn prediction is high dimensional problem....
Yihui Qiu, Hong Li
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
ECCV
2010
Springer
13 years 10 months ago
Randomized Locality Sensitive Vocabularies for Bag-of-Features Model
Abstract. Visual vocabulary construction is an integral part of the popular Bag-of-Features (BOF) model. When visual data scale up (in terms of the dimensionality of features or/an...