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FSS
2007
102views more  FSS 2007»
13 years 8 months ago
Extraction of fuzzy rules from support vector machines
The relationship between support vector machines (SVMs) and Takagi–Sugeno–Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for ...
Juan Luis Castro, L. D. Flores-Hidalgo, Carlos Jav...
PR
2007
104views more  PR 2007»
13 years 8 months ago
Optimizing resources in model selection for support vector machine
Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the...
Mathias M. Adankon, Mohamed Cheriet
FCSC
2010
92views more  FCSC 2010»
13 years 7 months ago
TRainbow: a new trusted virtual machine based platform
Currently, with the evolution of virtualization technology, cloud computing mode has become more and more popular. However, people still concern the issues of the runtime integrity...
Yuzhong Sun, Haifeng Fang, Ying Song, Lei Du, Kai ...
ICASSP
2011
IEEE
13 years 18 days ago
Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications
Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting th...
Kyong-Ho Lee, Sun-Yuan Kung, Naveen Verma
ICML
2008
IEEE
14 years 9 months ago
An RKHS for multi-view learning and manifold co-regularization
Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs)...
Vikas Sindhwani, David S. Rosenberg