distinct meanings in different branches of statistics. In statistics, especially in Bayesian statistics, the kernel of a probability density function (pdf)...

In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method...

such as "pricing kernel" or stochastic discount factor, or the smoothing of data recovered from robust statistics; see kernel (statistics). The precursor...

up kernel in Wiktionary, the free dictionary. Kernel may refer to: Kernel (operating system), the central component of most operating systems Kernel (image...

The Linux kernel is a free and open source: 4  Unix-like kernel that is used in many computer systems worldwide. The kernel was created by Linus Torvalds...

learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular,...

In statistics, the order of a kernel is the degree of the first non-zero moment of a kernel. The literature knows two major definitions of the order of...

In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These...

In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a...

generate a dimensionless metric of three "load average" numbers in the kernel. Users can easily query the current result from a Unix shell by running...

important addition to multivariate statistics. Based on research carried out in the 1990s and 2000s, multivariate kernel density estimation has reached a...

open source Unix-like operating systems based on the Linux kernel, an operating system kernel first released on September 17, 1991, by Linus Torvalds. Linux...

multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods...

distribution Kernel density estimation Kernel Fisher discriminant analysis Kernel methods Kernel principal component analysis Kernel regression Kernel smoother...

computing, an oops is a serious but non-fatal error in the Linux kernel. An oops may precede a kernel panic, but it may also allow continued operation with compromised...

Numerical integration Orthogonality Weighted mean Linear combination Kernel (statistics) Measure (mathematics) Riemann–Stieltjes integral Weighting Window...

In functional analysis, a reproducing kernel Hilbert space (RKHS) is a Hilbert space of functions in which point evaluation is a continuous linear functional...

In probability theory, a Markov kernel (also known as a stochastic kernel or probability kernel) is a map that in the general theory of Markov processes...

Intel oneAPI Math Kernel Library (Intel oneMKL), formerly known as Intel Math Kernel Library, is a library of optimized math routines for science, engineering...

A kernel smoother is a statistical technique to estimate a real valued function f : R p → R {\displaystyle f:\mathbb {R} ^{p}\to \mathbb {R} } as the weighted...

In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which...

neighboring points lookup DBSCAN OPTICS algorithm Kernel density estimation (KDE) Kernel (statistics) Cheng, Yizong (August 1995). "Mean Shift, Mode Seeking...

In statistics, adaptive or "variable-bandwidth" kernel density estimation is a form of kernel density estimation in which the size of the kernels used...

Palm kernel oil is an edible plant oil derived from the kernel of the oil palm tree Elaeis guineensis. It is related to two other edible oils: palm oil...

study of artificial neural networks (ANNs), the neural tangent kernel (NTK) is a kernel that describes the evolution of deep artificial neural networks...

estimator Efficiency (statistics) Completeness (statistics) Non-parametric statistics Nonparametric regression Kernels Kernel method Statistical learning...

Statistics & Probability Letters. 94: 230–235. arXiv:1403.2215. doi:10.1016/j.spl.2014.07.030. Driscoll, Michael F. (1973). "The reproducing kernel Hilbert...

interpretation of kernel regularization examines how kernel methods in machine learning can be understood through the lens of Bayesian statistics, a framework...

supporting hardware. CPU Utilisation and Memory statistics Resources and Kernel/load average statistics top Processes sorted by CPU used On AIX, there...

using the kernel trick, representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function...