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» Listwise approach to learning to rank: theory and algorithm
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AIR
2004
113views more  AIR 2004»
13 years 8 months ago
Class Noise vs. Attribute Noise: A Quantitative Study
Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on...
Xingquan Zhu, Xindong Wu
WSDM
2010
ACM
215views Data Mining» more  WSDM 2010»
14 years 6 months ago
GeoFolk: Latent spatial semantics in Web 2.0 social media
We describe an approach for multi-modal characterization of social media by combining text features (e.g. tags as a prominent example of short, unstructured text labels) with spat...
Sergej Sizov
DAC
2005
ACM
14 years 10 months ago
Efficient SAT solving: beyond supercubes
SAT (Boolean satisfiability) has become the primary Boolean reasoning engine for many EDA applications, so the efficiency of SAT solving is of great practical importance. Recently...
Domagoj Babic, Jesse D. Bingham, Alan J. Hu
ICIP
2009
IEEE
13 years 6 months ago
Selecting representative and distinctive descriptors for efficient landmark recognition
To have a robust and informative image content representation for image categorization, we often need to extract as many as possible visual features at various locations, scales a...
Sheng Gao, Joo-Hwee Lim
ICCV
2001
IEEE
14 years 11 months ago
Robust Principal Component Analysis for Computer Vision
Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance, and motion. One drawback of typical PCA methods is that they are least squares...
Fernando De la Torre, Michael J. Black