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Minimizing inter-subject variability in fNIRS based Brain Computer Interfaces via multiple-kernel support vector learning
Brain Computer Interfaces Functional Near-Infrared Spectroscopy Inter-subject variability Support Vector Machines RKHS
2012/9/23
Functional Near-Infrared spectroscopy (fNIRS) is an emerging non-invasive brain computer interface (BCI) modality that measures changes in haemoglobin concentrations in the cortical tissue. To date mo...
Solving Support Vector Machines in Reproducing Kernel Banach Spaces with Positive Definite Functions
support vector machine reproducing kernel Banach space reproducing kernel,posi-tive definite function Fourier transform,Sobolev space,Matern function,Sobolev spline
2012/9/22
In this paper we extend support vector machines from reproducing kernel Hilbert spaces into reproducing kernel Banach spaces whose reproducing kernels can be defined on nonsymmetric domains. Using the...
Kernel density estimation via diffusion and the complex exponentials approximation problem
condensed density random matrices parabolic PDE
2012/6/5
A kernel method is proposed to estimate the condensed density of the generalized eigenvalues of pencils of Hankel matrices whose elements have a joint noncentral Gaussian distribution with nonidentica...
High-frequency sampling and kernel estimation for continuous-time moving average processes
CARMA process continuous-time moving average process discretely sampled process FICARMA process gamma kernel
2011/7/22
Abstract: Interest in continuous-time processes has increased rapidly in recent years, largely because of the high-frequency data available in many areas of application, particularly in finance and tu...
Functional kernel estimators of large conditional quantiles
Conditional quantiles heavy-tailed distributions functional kernel estimator extreme-value theory
2011/7/12
Abstract: We address the estimation of conditional quantiles when the covariate is functional and when the order of the quantiles converges to one as the sample size increases. In a first time, we inv...
Optimal learning rates for Kernel Conjugate Gradient regression
Optimal learning rates Kernel Conjugate Gradient regression
2010/9/30
We prove rates of convergence in the statistical sense for kernel-based least squares regression using a conjugate gradient algorithm, where regularization against overfit-ting is obtained by early st...