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» Learning from Multiple Sources of Inaccurate Data
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195
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CVPR
2012
IEEE
13 years 5 months ago
Unsupervised metric fusion by cross diffusion
Metric learning is a fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary inf...
Bo Wang, Jiayan Jiang, Wei Wang 0028, Zhi-Hua Zhou...
179
Voted
JPDC
2006
185views more  JPDC 2006»
15 years 3 months ago
Commodity cluster-based parallel processing of hyperspectral imagery
The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particul...
Antonio Plaza, David Valencia, Javier Plaza, Pablo...
147
Voted
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
16 years 3 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
137
Voted
MLMI
2004
Springer
15 years 8 months ago
Multistream Dynamic Bayesian Network for Meeting Segmentation
This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose e...
Alfred Dielmann, Steve Renals
IWDC
2001
Springer
15 years 7 months ago
Design and Deployment of a Passive Monitoring Infrastructure
Abstract— This paper presents the architecture of a passive monitoring system installed within the Sprint IP backbone network. This system differs from other packet monitoring sy...
Chuck Fraleigh, Christophe Diot, Bryan Lyles, Sue ...