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» Maximal Vector Computation in Large Data Sets
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ICML
2003
IEEE
14 years 10 months ago
Incorporating Diversity in Active Learning with Support Vector Machines
In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
Klaus Brinker
KDD
2005
ACM
117views Data Mining» more  KDD 2005»
14 years 9 months ago
Rule extraction from linear support vector machines
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
IVC
2006
120views more  IVC 2006»
13 years 9 months ago
Facial pose from 3D data
The distribution of the apparent 3D shape of human faces across the view-sphere is complex, owing to factors such as variations in identity, facial expression, minor occlusions an...
Ajit Rajwade, Martin D. Levine
DATAMINE
1998
145views more  DATAMINE 1998»
13 years 9 months ago
A Tutorial on Support Vector Machines for Pattern Recognition
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-...
Christopher J. C. Burges
VISSYM
2003
13 years 10 months ago
Improving Topological Segmentation of Three-dimensional Vector Fields
We present three enhancements to accelerate the extraction of separatrices of three-dimensional vector fields, using intelligently selected “sample” streamlines. These enhanc...
Karim Mahrous, Janine Bennett, Bernd Hamann, Kenne...