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» Classifier Selection Based on Data Complexity Measures
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CVPR
2004
IEEE
15 years 11 months ago
Feature Selection for Classifying High-Dimensional Numerical Data
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
Yimin Wu, Aidong Zhang
ICIAR
2009
Springer
14 years 7 months ago
A Robust Modular Wavelet Network Based Symbol Classifier
This paper presents a robust automatic shape classifier using modular wavelet networks (MWNs). A shape descriptor is constructed based on a combination of global geometric features...
Akshaya Kumar Mishra, Paul W. Fieguth, David A. Cl...
BMCBI
2005
190views more  BMCBI 2005»
14 years 9 months ago
An Entropy-based gene selection method for cancer classification using microarray data
Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of n...
Xiaoxing Liu, Arun Krishnan, Adrian Mondry
DATAMINE
2002
169views more  DATAMINE 2002»
14 years 9 months ago
Advances in Instance Selection for Instance-Based Learning Algorithms
The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time ...
Henry Brighton, Chris Mellish
HIS
2004
14 years 11 months ago
Adaptive Boosting with Leader based Learners for Classification of Large Handwritten Data
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...