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JMLR
2010
134views more  JMLR 2010»
13 years 16 days ago
Bayesian Algorithms for Causal Data Mining
We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which prec...
Subramani Mani, Constantin F. Aliferis, Alexander ...
TALG
2010
158views more  TALG 2010»
13 years 14 days ago
Clustering for metric and nonmetric distance measures
We study a generalization of the k-median problem with respect to an arbitrary dissimilarity measure D. Given a finite set P of size n, our goal is to find a set C of size k such t...
Marcel R. Ackermann, Johannes Blömer, Christi...
JAMIA
2011
98views more  JAMIA 2011»
12 years 8 months ago
Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network
Background Systematic study of clinical phenotypes is important for a better understanding of the genetic basis of human diseases and more effective gene-based disease management....
Jyotishman Pathak, Janey Wang, Sudha Kashyap, Meli...
CIKM
2011
Springer
12 years 5 months ago
Partial duplicate detection for large book collections
A framework is presented for discovering partial duplicates in large collections of scanned books with optical character recognition (OCR) errors. Each book in the collection is r...
Ismet Zeki Yalniz, Ethem F. Can, R. Manmatha
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
11 years 8 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman