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WSDM
2009
ACM
191views Data Mining» more  WSDM 2009»
16 years 1 months ago
Generating labels from clicks
The ranking function used by search engines to order results is learned from labeled training data. Each training point is a (query, URL) pair that is labeled by a human judge who...
Rakesh Agrawal, Alan Halverson, Krishnaram Kenthap...
ICDM
2008
IEEE
150views Data Mining» more  ICDM 2008»
16 years 1 months ago
Pseudolikelihood EM for Within-network Relational Learning
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
Rongjing Xiang, Jennifer Neville
ICDM
2008
IEEE
107views Data Mining» more  ICDM 2008»
16 years 1 months ago
Graph-Based Iterative Hybrid Feature Selection
When the number of labeled examples is limited, traditional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample problem. To ...
ErHeng Zhong, Sihong Xie, Wei Fan, Jiangtao Ren, J...
KDD
2007
ACM
124views Data Mining» more  KDD 2007»
16 years 24 days ago
Hierarchical mixture models: a probabilistic analysis
Mixture models form one of the most widely used classes of generative models for describing structured and clustered data. In this paper we develop a new approach for the analysis...
Mark Sandler
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
16 years 5 days ago
Combining partitions by probabilistic label aggregation
Data clustering represents an important tool in exploratory data analysis. The lack of objective criteria render model selection as well as the identification of robust solutions...
Tilman Lange, Joachim M. Buhmann
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