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» Data selection for support vector machine classifiers
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ICML
2004
IEEE
15 years 10 months ago
Improving SVM accuracy by training on auxiliary data sources
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second...
Pengcheng Wu, Thomas G. Dietterich
IJCNN
2008
IEEE
15 years 4 months ago
Feature selection based on kernel discriminant analysis for multi-class problems
— We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an -class problem, which finds eigenvectors on which the projected class data are loca...
Tsuneyoshi Ishii, Shigeo Abe
DCC
2006
IEEE
15 years 9 months ago
Compression and Machine Learning: A New Perspective on Feature Space Vectors
The use of compression algorithms in machine learning tasks such as clustering and classification has appeared in a variety of fields, sometimes with the promise of reducing probl...
D. Sculley, Carla E. Brodley
DAS
2008
Springer
14 years 11 months ago
New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets
In classification tasks, class-modular strategy has been widely used. It has outperformed classical strategy for pattern classification task in many applications [1]. However, in ...
Sami Gazzah, Najoua Essoukri Ben Amara
JCB
2000
107views more  JCB 2000»
14 years 9 months ago
A Discriminative Framework for Detecting Remote Protein Homologies
A new method for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support...
Tommi Jaakkola, Mark Diekhans, David Haussler