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» Training of Classifiers Using Virtual Samples Only
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CMIG
2011
168views more  CMIG 2011»
12 years 11 months ago
Comparing axial CT slices in quantized N-dimensional SURF descriptor space to estimate the visible body region
In this paper, a method is described to automatically estimate the visible body region of a computed tomography (CT) volume image. In order to quantify the body region, a body coo...
Johannes Feulner, Shaohua Kevin Zhou, Elli Angelop...
GECCO
2010
Springer
155views Optimization» more  GECCO 2010»
14 years 10 days ago
Negative selection algorithms without generating detectors
Negative selection algorithms are immune-inspired classifiers that are trained on negative examples only. Classification is performed by generating detectors that match none of ...
Maciej Liskiewicz, Johannes Textor
KES
2008
Springer
13 years 7 months ago
Downsizing Multigenic Predictors of the Response to Preoperative Chemotherapy in Breast Cancer
We present a method for designing efficient multigenic predictors with few probes and its application to the prediction of the response to preoperative chemotherapy in breast cance...
René Natowicz, Roberto Incitti, Roman Rouzi...
DAGM
2011
Springer
12 years 7 months ago
Agnostic Domain Adaptation
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
Alexander Vezhnevets, Joachim M. Buhmann
BMCBI
2005
90views more  BMCBI 2005»
13 years 7 months ago
TMB-Hunt: An amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins
Background: Beta-barrel transmembrane (bbtm) proteins are a functionally important and diverse group of proteins expressed in the outer membranes of bacteria (both gram negative a...
Andrew G. Garrow, Alison Agnew, David R. Westhead