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» Query-level loss functions for information retrieval
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ICML
2007
IEEE
14 years 5 months ago
Learning to rank: from pairwise approach to listwise approach
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...
NIPS
2008
13 years 6 months ago
Structured ranking learning using cumulative distribution networks
Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
Jim C. Huang, Brendan J. Frey
SIGIR
2008
ACM
13 years 4 months ago
Query dependent ranking using K-nearest neighbor
Many ranking models have been proposed in information retrieval, and recently machine learning techniques have also been applied to ranking model construction. Most of the existin...
Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, H...
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
14 years 5 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
BMCBI
2008
187views more  BMCBI 2008»
13 years 5 months ago
PSAT: A web tool to compare genomic neighborhoods of multiple prokaryotic genomes
Background: The conservation of gene order among prokaryotic genomes can provide valuable insight into gene function, protein interactions, or events by which genomes have evolved...
Christine Fong, Laurence Rohmer, Matthew Radey, Mi...