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ICML
2007
IEEE
16 years 5 months ago
Discriminant kernel and regularization parameter learning via semidefinite programming
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection o...
Jieping Ye, Jianhui Chen, Shuiwang Ji
BMCBI
2006
202views more  BMCBI 2006»
15 years 4 months ago
Spectral embedding finds meaningful (relevant) structure in image and microarray data
Background: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing ...
Brandon W. Higgs, Jennifer W. Weller, Jeffrey L. S...
SIGGRAPH
2000
ACM
15 years 7 months ago
Out-of-core simplification of large polygonal models
We present an algorithm for out-of-core simplification of large polygonal datasets that are too complex to fit in main memory. The algorithm extends the vertex clustering scheme o...
Peter Lindstrom
ICDM
2009
IEEE
134views Data Mining» more  ICDM 2009»
15 years 2 months ago
Efficient Discovery of Confounders in Large Data Sets
Given a large transaction database, association analysis is concerned with efficiently finding strongly related objects. Unlike traditional associate analysis, where relationships ...
Wenjun Zhou, Hui Xiong
GECCO
2008
Springer
184views Optimization» more  GECCO 2008»
15 years 5 months ago
Analysis of mammography reports using maximum variation sampling
A genetic algorithm (GA) was developed to implement a maximum variation sampling technique to derive a subset of data from a large dataset of unstructured mammography reports. It ...
Robert M. Patton, Barbara G. Beckerman, Thomas E. ...