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CORR
2010
Springer
219views Education» more  CORR 2010»
13 years 8 months ago
Clustering high dimensional data using subspace and projected clustering algorithms
: Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerate...
Rahmat Widia Sembiring, Jasni Mohamad Zain, Abdull...
JUCS
2008
155views more  JUCS 2008»
13 years 7 months ago
Combining Classifiers in the ROC-space for Off-line Signature Verification
: In this work we present a strategy for off-line signature verification. It takes into account a writer-independent model which reduces the pattern recognition problem to a 2-clas...
Luiz S. Oliveira, Edson J. R. Justino, Robert Sabo...
PR
2006
229views more  PR 2006»
13 years 7 months ago
FS_SFS: A novel feature selection method for support vector machines
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of "overfitting". Feature Selection addre...
Yi Liu, Yuan F. Zheng
ACSC
2005
IEEE
14 years 1 months ago
The Electronic Primaries: Predicting the U.S. Presidency Using Feature Selection with Safe Data Reduction
The data mining inspired problem of finding the critical, and most useful features to be used to classify a data set, and construct rules to predict the class of future examples ...
Pablo Moscato, Luke Mathieson, Alexandre Mendes, R...
CORR
2010
Springer
81views Education» more  CORR 2010»
13 years 2 months ago
Analysis of Agglomerative Clustering
The diameter k-clustering problem is the problem of partitioning a finite subset of Rd into k subsets called clusters such that the maximum diameter of the clusters is minimized. ...
Marcel R. Ackermann, Johannes Blömer, Daniel ...