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» Evaluating Feature Selection for SVMs in High Dimensions
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ICML
2010
IEEE
13 years 6 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
ACIVS
2006
Springer
13 years 11 months ago
Dedicated Hardware for Real-Time Computation of Second-Order Statistical Features for High Resolution Images
We present a novel dedicated hardware system for the extraction of second-order statistical features from high-resolution images. The selected features are based on gray level co-o...
Dimitris G. Bariamis, Dimitrios K. Iakovidis, Dimi...
CVPR
2011
IEEE
13 years 13 days ago
Proposal Generation for Object Detection using Cascaded Ranking SVMs
Object recognition has made great strides recently. However, the best methods, such as those based on kernelSVMs are highly computationally intensive. The problem of how to accele...
Ziming Zhang, Jonathan Warrell, Philip Torr
PRL
2006
180views more  PRL 2006»
13 years 5 months ago
MutualBoost learning for selecting Gabor features for face recognition
This paper describes an improved boosting algorithm, the MutualBoost algorithm, and its application in developing a fast and robust Gabor feature based face recognition system. Th...
LinLin Shen, Li Bai
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
11 years 7 months ago
Mining emerging patterns by streaming feature selection
Building an accurate emerging pattern classifier with a highdimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unava...
Kui Yu, Wei Ding 0003, Dan A. Simovici, Xindong Wu