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» A Simple Decomposition Method for Support Vector Machines
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BMCBI
2006
110views more  BMCBI 2006»
13 years 9 months ago
Bias in error estimation when using cross-validation for model selection
Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers...
Sudhir Varma, Richard Simon
BMCBI
2007
157views more  BMCBI 2007»
13 years 9 months ago
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational
Background: High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination wi...
Nico Pfeifer, Andreas Leinenbach, Christian G. Hub...
ICPR
2008
IEEE
14 years 10 months ago
Multiple kernel learning from sets of partially matching image features
Abstract: Kernel classifiers based on Support Vector Machines (SVM) have achieved state-ofthe-art results in several visual classification tasks, however, recent publications and d...
Guo ShengYang, Min Tan, Si-Yao Fu, Zeng-Guang Hou,...
IMSCCS
2007
IEEE
14 years 3 months ago
Asymmetric Bagging and Feature Selection for Activities Prediction of Drug Molecules
Background: Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cy...
Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang, Jack Y. ...
ISBI
2007
IEEE
14 years 3 months ago
Automated Grading of Prostate Cancer Using Architectural and Textural Image Features
The current method of grading prostate cancer on histology uses the Gleason system, which describes five increasingly malignant stages of cancer according to qualitative analysis...
Scott Doyle, Mark Hwang, Kinsuk Shah, Anant Madabh...