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» Feature Selection and Effective Classifiers
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ICPR
2004
IEEE
14 years 9 months ago
Multi-Class Extensions of the GLDB Feature Extraction Algorithm for Spectral Data
The Generalized Local Discriminant Bases (GLDB) algorithm proposed by Kumar, Ghosh and Crawford in [4], is a effective feature extraction method for spectral data. It identifies g...
Pavel Paclík, Robert P. W. Duin, Serguei Ve...
ICMCS
2006
IEEE
151views Multimedia» more  ICMCS 2006»
14 years 2 months ago
Support Vector Machine for Multiple Feature Classifcation
In this paper an effective method of using SVM classifier for multiple feature classification is proposed. Compared with traditional combination methods where all needed base clas...
Bing-Yu Sun, Moon-Chuen Lee
ESANN
2003
13 years 10 months ago
Searching optimal feature subset using mutual information
A novel feature selection methodology is proposed with the concept of mutual information. The proposed methodology effectively circumvents two major problems in feature selection ...
D. Huang, Tommy W. S. Chow
AI
2004
Springer
13 years 8 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and imp...
Huan Liu, Hiroshi Motoda, Lei Yu
KDD
2009
ACM
158views Data Mining» more  KDD 2009»
14 years 9 months ago
Feature shaping for linear SVM classifiers
: ? Feature Shaping for Linear SVM Classifiers George Forman, Martin Scholz, Shyamsundar Rajaram HP Laboratories HPL-2009-31R1 text classification machine learning, feature weighti...
George Forman, Martin Scholz, Shyamsundar Rajaram