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» On Feature Selection, Bias-Variance, and Bagging
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KDD
2005
ACM
205views Data Mining» more  KDD 2005»
14 years 1 months ago
Feature bagging for outlier detection
Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel feature bagging approach for detecting outliers in...
Aleksandar Lazarevic, Vipin Kumar
BMCBI
2006
165views more  BMCBI 2006»
13 years 7 months ago
Improved variance estimation of classification performance via reduction of bias caused by small sample size
Background: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm tha...
Ulrika Wickenberg-Bolin, Hanna Göransson, M&a...
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
ISDA
2010
IEEE
13 years 5 months ago
Feature selection is the ReliefF for multiple instance learning
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
ACCV
2010
Springer
13 years 2 months ago
Efficient Visual Object Tracking with Online Nearest Neighbor Classifier
Abstract. A tracking-by-detection framework is proposed that combines nearest-neighbor classification of bags of features, efficient subwindow search, and a novel feature selection...
Steve Gu, Ying Zheng, Carlo Tomasi