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TITB
2008
102views more  TITB 2008»
14 years 10 months ago
Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kern
Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their hig...
Baek Hwan Cho, Hwanjo Yu, Jong Shill Lee, Young Jo...
BMCBI
2005
251views more  BMCBI 2005»
14 years 10 months ago
Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very effici...
Tapio Pahikkala, Filip Ginter, Jorma Boberg, Jouni...
BMCBI
2004
252views more  BMCBI 2004»
14 years 10 months ago
Applying Support Vector Machines for Gene ontology based gene function prediction
Background: The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to ann...
Arunachalam Vinayagam, Rainer König, Jutta Mo...
BMCBI
2010
104views more  BMCBI 2010»
14 years 10 months ago
Response network analysis of differential gene expression in human epithelial lung cells during avian influenza infections
Background: The recent emergence of the H5N1 influenza virus from avian reservoirs has raised concern about future influenza strains of high virulence emerging that could easily i...
Ken Tatebe, Ahmet Zeytun, Ruy M. Ribeiro, Robert H...
ICPR
2008
IEEE
15 years 4 months ago
Fast model selection for MaxMinOver-based training of support vector machines
OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model select...
Fabian Timm, Sascha Klement, Thomas Martinetz