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» Adapting SVM Classifiers to Data with Shifted Distributions
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CORR
2000
Springer
84views Education» more  CORR 2000»
13 years 9 months ago
Robust Classification for Imprecise Environments
In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building ro...
Foster J. Provost, Tom Fawcett
FGR
2004
IEEE
167views Biometrics» more  FGR 2004»
14 years 24 days ago
Adaptive Learning of an Accurate Skin-Color Model
Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture t...
Qiang Zhu, Kwang-Ting Cheng, Ching-Tung Wu, Yi-Leh...
PAMI
2010
132views more  PAMI 2010»
13 years 7 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Tobias Glasmachers, Christian Igel
MICCAI
2009
Springer
14 years 10 months ago
MKL for Robust Multi-modality AD Classification
We study the problem of classifying mild Alzheimer's disease (AD) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification...
Chris Hinrichs, Vikas Singh, Guofan Xu, Sterlin...
KDD
2008
ACM
104views Data Mining» more  KDD 2008»
14 years 9 months ago
Learning methods for lung tumor markerless gating in image-guided radiotherapy
In an idealized gated radiotherapy treatment, radiation is delivered only when the tumor is at the right position. For gated lung cancer radiotherapy, it is difficult to generate ...
Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian ...