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» Computing LTS Regression for Large Data Sets
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KDD
2001
ACM
253views Data Mining» more  KDD 2001»
14 years 8 months ago
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
Jens-Peter Dittrich, Bernhard Seeger
WSC
2004
13 years 9 months ago
Using Simulated Data in Support of Research on Regression Analysis
Using simulated data to develop and study diagnostic tools for data analysis is very beneficial. The user can gain insight about what happens when assumptions are violated since t...
Christopher Michael Hill, Linda C. Malone
PAMI
2008
139views more  PAMI 2008»
13 years 7 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...
CSDA
2011
12 years 11 months ago
Iterative stepwise regression imputation using standard and robust methods
Imputation of missing values is one of the major tasks for data pre-processing in many areas. Whenever imputation of data from official statistics comes into mind, several (additi...
Matthias Templ, Alexander Kowarik, Peter Filzmoser
ICDM
2008
IEEE
156views Data Mining» more  ICDM 2008»
14 years 2 months ago
Exploiting Local and Global Invariants for the Management of Large Scale Information Systems
This paper presents a data oriented approach to modeling the complex computing systems, in which an ensemble of correlation models are discovered to represent the system status. I...
Haifeng Chen, Haibin Cheng, Guofei Jiang, Kenji Yo...