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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 .
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 ...
The kernel is a computer program at the core of a computer's operating system and generally has complete control over everything in the system. The kernel is also responsible for preventing and mitigating conflicts between different processes. [1] It is the portion of the operating system code that is always resident in memory [2] and ...
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 smoother. A kernel smoother is a statistical technique to estimate a real valued function as the weighted average of neighboring observed data. The weight is defined by the kernel, such that closer points are given higher weights. The estimated function is smooth, and the level of smoothness is set by a single parameter.
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 machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1] The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings ...