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» Predicting labels for dyadic data
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ICML
2005
IEEE
14 years 10 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan
BMCBI
2010
183views more  BMCBI 2010»
13 years 10 months ago
Active learning for human protein-protein interaction prediction
Background: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is reso...
Thahir P. Mohamed, Jaime G. Carbonell, Madhavi Gan...
ESWA
2008
134views more  ESWA 2008»
13 years 10 months ago
A novel method for measuring semantic similarity for XML schema matching
Enterprises integration has recently gained great attentions, as never before. The paper deals with an essential activity enabling seamless enterprises integration, that is, a sim...
Buhwan Jeong, Daewon Lee, Hyunbo Cho, Jaewook Lee
ICMLA
2007
13 years 11 months ago
Machine learned regression for abductive DNA sequencing
We construct machine learned regressors to predict the behaviour of DNA sequencing data from the fluorescent labelled Sanger method. These predictions are used to assess hypothes...
David Thornley, Maxim Zverev, Stavros Petridis
CVPR
2010
IEEE
14 years 3 months ago
YouTubeCat: Learning to Categorize Wild Web Videos
Automatic categorization of videos in a Web-scale unconstrained collection such as YouTube is a challenging task. A key issue is how to build an effective training set in the pres...
Zheshen Wang, Ming Zhao, Yang Song, Sanjiv Kumar, ...