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ICML
2008
IEEE
14 years 11 months ago
Metric embedding for kernel classification rules
In this paper, we consider a smoothing kernelbased classification rule and propose an algorithm for optimizing the performance of the rule by learning the bandwidth of the smoothi...
Bharath K. Sriperumbudur, Omer A. Lang, Gert R. G....
COLT
2010
Springer
13 years 9 months ago
Efficient Classification for Metric Data
Recent advances in large-margin classification of data residing in general metric spaces (rather than Hilbert spaces) enable classification under various natural metrics, such as ...
Lee-Ad Gottlieb, Leonid Kontorovich, Robert Krauth...
ICML
2007
IEEE
14 years 11 months ago
Optimal dimensionality of metric space for classification
In many real-world applications, Euclidean distance in the original space is not good due to the curse of dimensionality. In this paper, we propose a new method, called Discrimina...
Wei Zhang, Xiangyang Xue, Zichen Sun, Yue-Fei Guo,...
VCIP
2003
14 years 6 days ago
Three-dimensional mesh simplification using normal variation error metric and modified subdivided edge classification
In order to transmit or store three-dimensional (3-D) mesh models efficiently, we need to simplify them. Although the quadric error metric (QEM) provides fast and accurate geometr...
Eun-Young Chang, Chung-Hyun Ahn, Yo-Sung Ho
NN
2006
Springer
146views Neural Networks» more  NN 2006»
13 years 10 months ago
Comparison of relevance learning vector quantization with other metric adaptive classification methods
The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation...
Thomas Villmann, Frank-Michael Schleif, Barbara Ha...