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PAMI
2008
139views more  PAMI 2008»
13 years 7 months ago
A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
We consider the problem of learning a ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an -accurate approxim...
Vikas C. Raykar, Ramani Duraiswami, Balaji Krishna...
IRI
2007
IEEE
14 years 1 months ago
BESearch: A Supervised Learning Approach to Search for Molecular Event Participants
Biomedical researchers rely on keyword-based search engines to retrieve superficially relevant documents, from which they must filter out irrelevant information manually. Hence, t...
Richard Tzong-Han Tsai, Hong-Jie Dai, Hsi-Chuan Hu...
CORR
2008
Springer
116views Education» more  CORR 2008»
13 years 7 months ago
Learning to rank with combinatorial Hodge theory
Abstract. We propose a number of techniques for learning a global ranking from data that may be incomplete and imbalanced -- characteristics that are almost universal to modern dat...
Xiaoye Jiang, Lek-Heng Lim, Yuan Yao, Yinyu Ye
SDM
2007
SIAM
169views Data Mining» more  SDM 2007»
13 years 9 months ago
Rank Aggregation for Similar Items
The problem of combining the ranked preferences of many experts is an old and surprisingly deep problem that has gained renewed importance in many machine learning, data mining, a...
D. Sculley
ICCPOL
2009
Springer
13 years 5 months ago
A Simple and Efficient Model Pruning Method for Conditional Random Fields
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...
Hai Zhao, Chunyu Kit