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» Learning to rank with partially-labeled data
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SIGIR
2008
ACM
14 years 11 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
WSDM
2012
ACM
352views Data Mining» more  WSDM 2012»
13 years 7 months ago
Multi-relational matrix factorization using bayesian personalized ranking for social network data
A key element of the social networks on the internet such as Facebook and Flickr is that they encourage users to create connections between themselves, other users and objects. On...
Artus Krohn-Grimberghe, Lucas Drumond, Christoph F...
MM
2005
ACM
145views Multimedia» more  MM 2005»
15 years 5 months ago
Multiple instance learning for labeling faces in broadcasting news video
Labeling faces in news video with their names is an interesting research problem which was previously solved using supervised methods that demand significant user efforts on lab...
Jun Yang 0003, Rong Yan, Alexander G. Hauptmann
ICIP
2010
IEEE
14 years 9 months ago
Semi-supervised regression with temporal image sequences
We consider a semi-supervised regression setting where we have temporal sequences of partially labeled data, under the assumption that the labels should vary slowly along a sequen...
Ling Xie, Miguel Á. Carreira-Perpiñ&...
ICANN
2011
Springer
14 years 3 months ago
Semi-supervised Learning for WLAN Positioning
Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a “radio map” is constructed by modeling how the signal strength measureme...
Teemu Pulkkinen, Teemu Roos, Petri Myllymäki