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» Learning from Multiple Sources of Inaccurate Data
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ICTAI
2006
IEEE
14 years 2 months ago
MI-Winnow: A New Multiple-Instance Learning Algorithm
We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a co...
Sharath R. Cholleti, Sally A. Goldman, Rouhollah R...
MACOM
2010
13 years 3 months ago
On the Performance of Single LDGM Codes for Iterative Data Fusion over the Multiple Access Channel
One of the applications of wireless sensor networks currently undergoing active research focuses on the scenario where the information generated by a data source S is simultaneousl...
Javier Del Ser, Javier Garcia-Frias, Pedro M. Cres...
BMCBI
2007
174views more  BMCBI 2007»
13 years 9 months ago
Normalization method for metabolomics data using optimal selection of multiple internal standards
Background: Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific so...
Marko Sysi-Aho, Mikko Katajamaa, Laxman Yetukuri, ...
IJCAI
2003
13 years 10 months ago
Learning Value Predictors for the Speculative Execution of Information Gathering Plans
Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is clos...
Greg Barish, Craig A. Knoblock
PAMI
2012
11 years 11 months ago
Domain Transfer Multiple Kernel Learning
—Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has ...
Lixin Duan, Ivor W. Tsang, Dong Xu