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» On the Learnability of Vector Spaces
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NIPS
2003
13 years 9 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
BMCBI
2010
243views more  BMCBI 2010»
13 years 8 months ago
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data
Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new ...
Christoph Bartenhagen, Hans-Ulrich Klein, Christia...
BMCBI
2008
118views more  BMCBI 2008»
13 years 8 months ago
Virtual screening of GPCRs: An in silico chemogenomics approach
The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules is ...
Laurent Jacob, Brice Hoffmann, Véronique St...
ALMOB
2006
113views more  ALMOB 2006»
13 years 8 months ago
Inverse bifurcation analysis: application to simple gene systems
Background: Bifurcation analysis has proven to be a powerful method for understanding the qualitative behavior of gene regulatory networks. In addition to the more traditional for...
James Lu, Heinz W. Engl, Peter Schuster
BMCBI
2006
173views more  BMCBI 2006»
13 years 8 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen