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Kernel Fisher discriminant analysis. In statistics, kernel Fisher discriminant analysis (KFD), [1] also known as generalized discriminant analysis [2] and kernel discriminant analysis, [3] is a kernelized version of linear discriminant analysis (LDA). It is named after Ronald Fisher .
In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in kernel density estimation to estimate random variables ' density functions, or in kernel regression to estimate the conditional expectation of a random variable. Kernels are also used in time-series, in the use of the ...
Kernel regression. In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y . In any nonparametric regression, the conditional expectation of a variable relative to a variable may be written:
Kernel principal component analysis. In the field of multivariate statistics, kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space .
In functional analysis (a branch of mathematics ), a reproducing kernel Hilbert space ( RKHS) is a Hilbert space of functions in which point evaluation is a continuous linear functional. Roughly speaking, this means that if two functions and in the RKHS are close in norm, i.e., is small, then and are also pointwise close, i.e., is small for all .
After recompiling a kernel binary image from source code, a kernel panic while booting the resulting kernel is a common problem if the kernel was not correctly configured, compiled or installed. Add-on hardware or malfunctioning RAM could also be sources of fatal kernel errors during start up, due to incompatibility with the OS or a missing ...
Neural tangent kernel. In the study of artificial neural networks (ANNs), the neural tangent kernel ( NTK) is a kernel that describes the evolution of deep artificial neural networks during their training by gradient descent. It allows ANNs to be studied using theoretical tools from kernel methods .
Kernelization. In computer science, a kernelization is a technique for designing efficient algorithms that achieve their efficiency by a preprocessing stage in which inputs to the algorithm are replaced by a smaller input, called a "kernel". The result of solving the problem on the kernel should either be the same as on the original input, or ...