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All decoding analyses were performed with MATLAB R2013a using radial basis function kernel SVM.
The improvement is achieved by using radial basis functions called osculatory radial basis functions.
Recognition 'units' in this style respond more like radial basis function units than elementary sigmoid units.
Secondly, Radial Basis Function (RBF) network is developed.
The method is structured on multiquadrics radial basis functions.
Various radial basis function neurons and training algorithms are considered.
Improved structure backpropagation and radial basis function networks are developed.
Radial basis functions approximated the constraint and the objective functions.
The nonlinearity is introduced by using radial basis functions.
Sparse radial basis function classification as a visualization technique leverages recent advances in radial basis function learning via convex optimization.
Tests were conducted to establish the best polynomial and Gaussian RBF (radial basis function) kernels.
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