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JCP
2006
74views more  JCP 2006»
13 years 6 months ago
Local Boosting of Decision Stumps for Regression and Classification Problems
Numerous data mining problems involve an investigation of associations between features in heterogeneous datasets, where different prediction models can be more suitable for differ...
Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Pan...
TEC
2008
146views more  TEC 2008»
13 years 6 months ago
An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis
In general, the analysis of microarray data requires two steps: feature selection and classification. From a variety of feature selection methods and classifiers, it is difficult t...
Kyung-Joong Kim, Sung-Bae Cho
CVPR
2008
IEEE
14 years 8 months ago
Classification using intersection kernel support vector machines is efficient
Straightforward classification using kernelized SVMs requires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can ...
Subhransu Maji, Alexander C. Berg, Jitendra Malik
BMCBI
2008
186views more  BMCBI 2008»
13 years 6 months ago
Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases
Background: Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates...
Min Zhang, Dabao Zhang, Martin T. Wells
AICCSA
2006
IEEE
121views Hardware» more  AICCSA 2006»
13 years 8 months ago
Software Defect Prediction Using Regression via Classification
In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially automatically discr...
Stamatia Bibi, Grigorios Tsoumakas, Ioannis Stamel...