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» Listwise approach to learning to rank: theory and algorithm
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AI
2002
Springer
13 years 9 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
RECSYS
2009
ACM
14 years 3 months ago
Collaborative prediction and ranking with non-random missing data
A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative ï¬...
Benjamin M. Marlin, Richard S. Zemel
CIVR
2008
Springer
166views Image Analysis» more  CIVR 2008»
13 years 10 months ago
A probabilistic ranking framework using unobservable binary events for video search
Recent content-based video retrieval systems combine output of concept detectors (also known as high-level features) with text obtained through automatic speech recognition. This ...
Robin Aly, Djoerd Hiemstra, Arjen P. de Vries, Fra...
TREC
2004
13 years 10 months ago
Can We Get A Better Retrieval Function From Machine?
The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each document in the collect...
Weiguo Fan, Wensi Xi, Edward A. Fox, Li Wang
IJCV
2007
159views more  IJCV 2007»
13 years 9 months ago
Face Hallucination: Theory and Practice
In this paper, we study face hallucination, or synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-reso...
Ce Liu, Heung-Yeung Shum, William T. Freeman