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» Generalizing over Several Learning Settings
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SIGIR
2006
ACM
14 years 3 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
BMCBI
2006
142views more  BMCBI 2006»
13 years 9 months ago
Improving the Performance of SVM-RFE to Select Genes in Microarray Data
Background: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. ...
Yuanyuan Ding, Dawn Wilkins
DAGM
2010
Springer
13 years 10 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
GIS
2007
ACM
14 years 10 months ago
TerraStream: from elevation data to watershed hierarchies
We consider the problem of extracting a river network and a watershed hierarchy from a terrain given as a set of irregularly spaced points. We describe TerraStream, a "pipeli...
Andrew Danner, Thomas Mølhave, Ke Yi, Panka...
SIGIR
2002
ACM
13 years 8 months ago
Document clustering with committees
Document clustering is useful in many information retrieval tasks: document browsing, organization and viewing of retrieval results, generation of Yahoo-like hierarchies of docume...
Patrick Pantel, Dekang Lin