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IJCNN
2008
IEEE
15 years 12 months ago
Sparse support vector machines trained in the reduced empirical feature space
— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Kazuki Iwamura, Shigeo Abe
ADMA
2006
Springer
172views Data Mining» more  ADMA 2006»
15 years 11 months ago
Experimental Comparison of Feature Subset Selection Using GA and ACO Algorithm
Abstract. Practical pattern classification and knowledge discovery problems require selecting a useful subset of features from a much larger set to represent the patterns to be cl...
Keunjoon Lee, Jinu Joo, Jihoon Yang, Vasant Honava...
150
Voted
IWANN
2005
Springer
15 years 11 months ago
Heuristic Search over a Ranking for Feature Selection
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in hig...
Roberto Ruiz, José Cristóbal Riquelm...
NPL
2002
120views more  NPL 2002»
15 years 5 months ago
A Novel Measure for Quantifying the Topology Preservation of Self Organizing Feature Maps
Recently, feature maps have been applied to various problem domains.The success of some of these applications critically depends on whether feature maps are topologically ordered. ...
Mu-Chun Su, Hsiao-Te Chang, Chien-Hsing Chou
PKDD
2009
Springer
113views Data Mining» more  PKDD 2009»
16 years 2 days ago
Feature Selection for Density Level-Sets
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an e...
Marius Kloft, Shinichi Nakajima, Ulf Brefeld