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» Combining VTS model compensation and support vector machines
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CSB
2004
IEEE
135views Bioinformatics» more  CSB 2004»
13 years 9 months ago
Selection of Patient Samples and Genes for Outcome Prediction
Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computatio...
Huiqing Liu, Jinyan Li, Limsoon Wong
ICPR
2008
IEEE
14 years 6 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,...
BMCBI
2007
157views more  BMCBI 2007»
13 years 5 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...
MICCAI
2000
Springer
13 years 9 months ago
Small Sample Size Learning for Shape Analysis of Anatomical Structures
We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medic...
Polina Golland, W. Eric L. Grimson, Martha Elizabe...
NIPS
2003
13 years 7 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller