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BMCBI
2006
201views more  BMCBI 2006»
13 years 7 months ago
Gene selection algorithms for microarray data based on least squares support vector machine
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao
BMCBI
2006
111views more  BMCBI 2006»
13 years 7 months ago
Using ESTs to improve the accuracy of de novo gene prediction
Background: ESTs are a tremendous resource for determining the exon-intron structures of genes, but even extensive EST sequencing tends to leave many exons and genes untouched. Ge...
Chaochun Wei, Michael R. Brent
TMI
2010
172views more  TMI 2010»
13 years 6 months ago
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation
Abstract— We compared four automated methods for hippocampal segmentation using different machine learning algorithms (1) hierarchical AdaBoost, (2) Support Vector Machines (SVM)...
Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova...
ICARCV
2002
IEEE
109views Robotics» more  ICARCV 2002»
14 years 15 days ago
Quantized wavelet features and support vector machines for on-road vehicle detection
The focus of this work is on the problem of feature selection and classification for on-road vehicle detection. In particular, we propose using quantized Haar wavelet features an...
Zehang Sun, George Bebis, Ronald Miller
ICPP
2009
IEEE
14 years 2 months ago
Run to Potential: Sweep Coverage in Wireless Sensor Networks
—Wireless sensor networks have become a promising technology in monitoring physical world. In many applications with wireless sensor networks, it is essential to understand how w...
Min Xi, Kui Wu, Yong Qi, Jizhong Zhao, Yunhao Liu,...