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ICML
2008
IEEE
14 years 9 months ago
Training SVM with indefinite kernels
Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
Jianhui Chen, Jieping Ye
ICML
2006
IEEE
14 years 9 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
WWW
2008
ACM
14 years 9 months ago
Non-intrusive monitoring and service adaptation for WS-BPEL
Web service processes currently lack monitoring and dynamic (runtime) adaptation mechanisms. In highly dynamic processes, services frequently need to be exchanged due to a variety...
Oliver Moser, Florian Rosenberg, Schahram Dustdar
ICSE
2008
IEEE-ACM
14 years 9 months ago
VieDAME - flexible and robust BPEL processes through monitoring and adaptation
VieDAME is a tool for monitoring and dynamic service adaptation of BPEL processes. The tool monitors partner service interaction to compute Quality of Service (QoS) data and perfo...
Oliver Moser, Florian Rosenberg, Schahram Dustdar
CVPR
2010
IEEE
14 years 5 months ago
Online Multiclass LPBoost
Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boo...
Amir Saffari, Martin Godec, Thomas Pock, Christian...