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» The Kernel Trick for Distances
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SDM
2009
SIAM
161views Data Mining» more  SDM 2009»
14 years 4 months ago
Feature Weighted SVMs Using Receiver Operating Characteristics.
Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used...
Shaoyi Zhang, M. Maruf Hossain, Md. Rafiul Hassan,...
ICML
2008
IEEE
14 years 8 months ago
Nearest hyperdisk methods for high-dimensional classification
In high-dimensional classification problems it is infeasible to include enough training samples to cover the class regions densely. Irregularities in the resulting sparse sample d...
Hakan Cevikalp, Bill Triggs, Robi Polikar
JMLR
2008
148views more  JMLR 2008»
13 years 7 months ago
Linear-Time Computation of Similarity Measures for Sequential Data
Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of sim...
Konrad Rieck, Pavel Laskov
CVPR
2007
IEEE
14 years 9 months ago
On the Blind Classification of Time Series
We propose a cord distance in the space of dynamical models that takes into account their dynamics, including transients, output maps and input distributions. In data analysis app...
Alessandro Bissacco, Stefano Soatto
ICML
2003
IEEE
14 years 8 months ago
Adaptive Feature-Space Conformal Transformation for Imbalanced-Data Learning
When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determining the class boundary. To remedy this...
Gang Wu, Edward Y. Chang