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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
PAKDD
2009
ACM
263views Data Mining» more  PAKDD 2009»
14 years 4 days ago
Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
It is a challenging and important task to retrieve images from a large and highly varied image data set based on their visual contents. Problems like how to fill the semantic gap b...
Xin Chen, Xiaohua Hu, Xiajiong Shen
IJCV
2006
206views more  IJCV 2006»
13 years 5 months ago
Random Sampling for Subspace Face Recognition
Subspacefacerecognitionoftensuffersfromtwoproblems:(1)thetrainingsamplesetissmallcompared with the high dimensional feature vector; (2) the performance is sensitive to the subspace...
Xiaogang Wang, Xiaoou Tang
SSPR
2000
Springer
13 years 9 months ago
The Role of Combining Rules in Bagging and Boosting
To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are...
Marina Skurichina, Robert P. W. Duin
TSP
2008
90views more  TSP 2008»
13 years 5 months ago
Generalized Correlation Decomposition-Based Blind Channel Estimation in DS-CDMA Systems With Unknown Wide-Sense Stationary Noise
A new blind subspace-based channel estimation technique is proposed for direct-sequence code-division multiple access (DS-CDMA) systems operating in the presence of unknown wide-se...
Keyvan Zarifi, Alex B. Gershman