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» Selectivity Estimation using Probabilistic Models
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IJCNN
2007
IEEE
15 years 6 months ago
Probability Density Function Estimation Using Orthogonal Forward Regression
— Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression tec...
Sheng Chen, Xia Hong, Chris J. Harris
SDM
2009
SIAM
235views Data Mining» more  SDM 2009»
15 years 9 months ago
Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases.
As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstruc...
ChengXiang Zhai, Duo Zhang, Jiawei Han
ESANN
2000
15 years 1 months ago
A statistical model selection strategy applied to neural networks
In statistical modelling, an investigator must often choose a suitable model among a collection of viable candidates. There is no consensus in the research community on how such a...
Joaquín Pizarro Junquera, Elisa Guerrero V&...
BMCBI
2006
116views more  BMCBI 2006»
14 years 12 months ago
A model-based approach to selection of tag SNPs
Background: Single Nucleotide Polymorphisms (SNPs) are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identific...
Pierre Nicolas, Fengzhu Sun, Lei M. Li
ICDM
2009
IEEE
163views Data Mining» more  ICDM 2009»
15 years 6 months ago
Kernel Conditional Quantile Estimation via Reduction Revisited
Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among ...
Novi Quadrianto, Kristian Kersting, Mark D. Reid, ...