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» On the monotonization of the training set
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91
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ICPR
2008
IEEE
15 years 7 months 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 15 days 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 1 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
99
Voted
ISVC
2007
Springer
15 years 6 months 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
98
Voted
JMLR
2008
133views more  JMLR 2008»
15 years 16 days 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