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» A Fully Distributed Framework for Cost-Sensitive Data Mining
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SDM
2012
SIAM
237views Data Mining» more  SDM 2012»
11 years 7 months ago
A Distributed Kernel Summation Framework for General-Dimension Machine Learning
Kernel summations are a ubiquitous key computational bottleneck in many data analysis methods. In this paper, we attempt to marry, for the first time, the best relevant technique...
Dongryeol Lee, Richard W. Vuduc, Alexander G. Gray
KDD
2010
ACM
287views Data Mining» more  KDD 2010»
13 years 7 months ago
Designing efficient cascaded classifiers: tradeoff between accuracy and cost
We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages. The approach relies on the idea of optimizing soft cascades. In particular, inst...
Vikas C. Raykar, Balaji Krishnapuram, Shipeng Yu
JDWM
2007
86views more  JDWM 2007»
13 years 5 months ago
Predicting Future Customers via Ensembling Gradually Expanded Trees
Our LAMDAer team has won the PAKDD'06 Data Mining Competition (Open Category) Grand Champion. This report presents our solution to PAKDD'06 Data Mining Competition. Follo...
Yang Yu, De-Chuan Zhan, Xu-Ying Liu, Ming Li, Zhi-...
ICDM
2009
IEEE
110views Data Mining» more  ICDM 2009»
14 years 1 days ago
Finding Maximal Fully-Correlated Itemsets in Large Databases
—Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. Much previous research focuses on finding ...
Lian Duan, William Nick Street
PKDD
2005
Springer
136views Data Mining» more  PKDD 2005»
13 years 11 months ago
Weka4WS: A WSRF-Enabled Weka Toolkit for Distributed Data Mining on Grids
This paper presents Weka4WS, a framework that extends the Weka toolkit for supporting distributed data mining on Grid environments. Weka4WS adopts the emerging Web Services Resourc...
Domenico Talia, Paolo Trunfio, Oreste Verta