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» Using Data Compressors to Construct Rank Tests
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KDD
2008
ACM
147views Data Mining» more  KDD 2008»
15 years 10 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
102
Voted
CSDA
2007
136views more  CSDA 2007»
14 years 10 months ago
A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation
The maximum rank correlation (MRC) estimator was originally studied by Han [1987. Nonparametric analysis of a generalized regression model. J. Econometrics 35, 303–316] and Sher...
Hansheng Wang
KDD
2007
ACM
210views Data Mining» more  KDD 2007»
15 years 4 months ago
Machine learning for stock selection
In this paper, we propose a new method called Prototype Ranking (PR) designed for the stock selection problem. PR takes into account the huge size of real-world stock data and app...
Robert J. Yan, Charles X. Ling
74
Voted
BMCBI
2005
93views more  BMCBI 2005»
14 years 10 months ago
Two-part permutation tests for DNA methylation and microarray data
Background: One important application of microarray experiments is to identify differentially expressed genes. Often, small and negative expression levels were clipped-off to be e...
Markus Neuhäuser, Tanja Boes, Karl-Heinz J&ou...
WSDM
2010
ACM
266views Data Mining» more  WSDM 2010»
15 years 7 months ago
Gathering and Ranking Photos of Named Entities with High Precision, High Recall, and Diversity
Knowledge-sharing communities like Wikipedia and automated extraction methods like those of DBpedia enable the construction of large machine-processible knowledge bases with relat...
Bilyana Taneva, Mouna Kacimi, Gerhard Weikum