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ICPR
2008
IEEE
16 years 16 days ago
A supervised learning approach for imbalanced data sets
This paper presents a new learning approach for pattern classification applications involving imbalanced data sets. In this approach, a clustering technique is employed to resamp...
Giang Hoang Nguyen, Abdesselam Bouzerdoum, Son Lam...
JMLR
2006
89views more  JMLR 2006»
15 years 6 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
ESANN
2003
15 years 7 months ago
Extraction of fuzzy rules from trained neural network using evolutionary algorithm
This paper presents our approach to the rule extraction problem from trained neural network. A method called REX is briefly described. REX acquires a set of fuzzy rules using an ev...
Urszula Markowska-Kaczmar, Wojciech Trelak
ISVC
2007
Springer
16 years 8 days ago
Lip Contour Segmentation Using Kernel Methods and Level Sets
This paper proposes a novel method for segmenting lips from face images or video sequences. A non-linear learning method in the form of an SVM classifier is trained to recognise l...
A. Khan, William J. Christmas, Josef Kittler
JMLR
2008
133views more  JMLR 2008»
15 years 6 months ago
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classi...
Suhrid Balakrishnan, David Madigan