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» Sampling Methods for Unsupervised Learning
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BMCBI
2006
126views more  BMCBI 2006»
14 years 11 months ago
A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors
Background: The development of effective frameworks that permit an accurate diagnosis of tumors, especially in their early stages, remains a grand challenge in the field of bioinf...
Andreas Keller, Nicole Ludwig, Nicole Comtesse, An...
JAIR
2008
120views more  JAIR 2008»
14 years 11 months ago
Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach
Machine learning techniques are gaining prevalence in the production of a wide range of classifiers for complex real-world applications with nonuniform testing and misclassificati...
Saher Esmeir, Shaul Markovitch
98
Voted
TNN
2010
127views Management» more  TNN 2010»
14 years 5 months ago
RAMOBoost: ranked minority oversampling in boosting
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imba...
Sheng Chen, Haibo He, Edwardo A. Garcia
WILF
2007
Springer
170views Fuzzy Logic» more  WILF 2007»
15 years 5 months ago
Time-Series Alignment by Non-negative Multiple Generalized Canonical Correlation Analysis
Background: Quantitative analysis of differential protein expressions requires to align temporal elution measurements from liquid chromatography coupled to mass spectrometry (LC/M...
Bernd Fischer, Volker Roth, Joachim M. Buhmann
111
Voted
BMCBI
2007
173views more  BMCBI 2007»
14 years 11 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...