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CIARP
2004
Springer
13 years 11 months ago
New Bounds and Approximations for the Error of Linear Classifiers
In this paper, we derive lower and upper bounds for the probability of error for a linear classifier, where the random vectors representing the underlying classes obey the multivar...
Luís G. Rueda
BMCBI
2010
160views more  BMCBI 2010»
13 years 7 months ago
Extracting consistent knowledge from highly inconsistent cancer gene data sources
Background: Hundreds of genes that are causally implicated in oncogenesis have been found and collected in various databases. For efficient application of these abundant but diver...
Xue Gong, Ruihong Wu, Yuannv Zhang, Wenyuan Zhao, ...
BMCBI
2011
12 years 11 months ago
To aggregate or not to aggregate high-dimensional classifiers
Background: High-throughput functional genomics technologies generate large amount of data with hundreds or thousands of measurements per sample. The number of sample is usually m...
Cheng-Jian Xu, Huub C. J. Hoefsloot, Age K. Smilde
VLSISP
2010
254views more  VLSISP 2010»
13 years 5 months ago
Manifold Based Local Classifiers: Linear and Nonlinear Approaches
Abstract In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nea...
Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill ...
ICDM
2007
IEEE
248views Data Mining» more  ICDM 2007»
13 years 11 months ago
Adapting SVM Classifiers to Data with Shifted Distributions
Many data mining applications can benefit from adapting existing classifiers to new data with shifted distributions. In this paper, we present Adaptive Support Vector Machine (Ada...
Jun Yang 0003, Rong Yan, Alexander G. Hauptmann