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» Learning to Rank with Supplementary Data
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CVPR
2012
IEEE
11 years 7 months ago
Non-negative low rank and sparse graph for semi-supervised learning
Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel no...
Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma...
PAMI
2008
139views more  PAMI 2008»
13 years 4 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...
SPIRE
2010
Springer
13 years 3 months ago
Dual-Sorted Inverted Lists
Several IR tasks rely, to achieve high efficiency, on a single pervasive data structure called the inverted index. This is a mapping from the terms in a text collection to the docu...
Gonzalo Navarro, Simon J. Puglisi
ECML
2007
Springer
13 years 10 months ago
An Unsupervised Learning Algorithm for Rank Aggregation
Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as op...
Alexandre Klementiev, Dan Roth, Kevin Small
AAAI
2012
11 years 7 months ago
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping
Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) ...
Tomer Levinboim, Fei Sha