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» Covering Numbers for Support Vector Machines
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88
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FSS
2007
102views more  FSS 2007»
14 years 9 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...
73
Voted

Publication
265views
15 years 6 months ago
Effective 2D-3D Medical Image Registration using Support Vector Machine
Registration of pre-operative 3D volume dataset and intra-operative 2D images gradually becomes an important technique to assist radiologists in diagnosing complicated diseases. In...
Wenyuan Qi, Lixu Gu
PR
2006
229views more  PR 2006»
14 years 9 months ago
FS_SFS: A novel feature selection method for support vector machines
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of "overfitting". Feature Selection addre...
Yi Liu, Yuan F. Zheng
ICML
2004
IEEE
15 years 10 months ago
Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
Rong Jin, Huan Liu
ICPR
2006
IEEE
15 years 10 months ago
Mixture of Support Vector Machines for HMM based Speech Recognition
Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-opt...
Sven E. Krüger, Martin Schafföner, Marce...