Also, the choice of the free parameters in the 谓 - SVR algorithm is related to several bioelectric properties of the problem. Results suggest that 谓 -SVR with a Laplacian distance kernel can be a suitable alternative for improved resolution in current and emerging non-contact cardiac imaging ...
SVR Classical statistical features [153] SVDD Bispectrum based signal processing, statistical features [154] RSVDD Classical statistical features, incremental learning [155] FSVDD Classical statistical features, fuzzy degradation index [49] GMM Kernel principal component analysis and Exponentially weighted mo...
We propose to use a Dual Problem Signal Model formulation of the 谓-Support Vector Regression (谓-SVR) algorithm, with a Mercer Kernel given by Laplacian of the distance function accounting for quasielectrostatic field conditions. This new approach avoids the matrix inversion while providing with ...
where V is a loss function, ∥𝑓∥2𝐴∥f∥A2 is the norm of the function in the Reproducing Kernel Hilbert Space (RKHS) ℋ𝑘Hk, ∥𝑓∥2𝐼∥f∥I2 is the norm of the function in the low-dimensional manifold, and C, 𝛾𝐴γA, 𝛾𝐼γI are the regularization weight ...