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

The term kernel is used in statistical analysis to refer to a window function. The term "kernel" has several distinct meanings in different branches of...

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

It allows ANNs to be studied using theoretical tools from kernel methods. In general, a kernel is a positive-semidefinite symmetric function of two inputs...

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

that the kernel method could essentially replace the Volterra series representation, although noting that the latter is more intuitive. This method was developed...

are essential tools in the application of kernel methods to large-scale learning problems. Kernel methods (for instance, support vector machines or Gaussian...

In structure mining, a graph kernel is a kernel function that computes an inner product on graphs. Graph kernels can be intuitively understood as functions...

involving the large kernel matrix, a low-rank approximation to the matrix is often used in the kernel trick. Another common method is Platt's sequential...

two variables, that is called the kernel or nucleus of the transform. Some kernels have an associated inverse kernel K − 1 ( u , t ) {\displaystyle K^{-1}(u...

learning to approximate kernel methods, introduced by Ali Rahimi and Ben Recht in their 2007 paper "Random Features for Large-Scale Kernel Machines", and extended...

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

kernel is a computer program at the core of a computer's operating system that always has complete control over everything in the system. The kernel is...

computer scientist known for his work in machine learning, especially on kernel methods and causality. He is a director at the Max Planck Institute for Intelligent...

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

data is well-supported) discriminative methods can have flexible criteria and yield better results. The Fisher kernel makes use of the Fisher score, defined...

best known methods for constructing utilization distributions are the so-called bivariate Gaussian or normal distribution kernel density methods. More recently...

In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers...

Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate...

approximated as a sum over kernels, whose domain is the feature space. If this is done in a reproducing kernel Hilbert space, a kernel method can be a universal...

learner. In machine learning, kernel random forests (KeRF) establish the connection between random forests and kernel methods. By slightly modifying their...

vortex method (DVM) Reproducing Kernel Particle Method (RKPM) (1995) Generalized/Gradient Reproducing Kernel Particle Method (2011) Finite mass method (FMM)...

{\displaystyle {\mathcal {H}}(R)} be a reproducing kernel Hilbert space with positive definite kernel R {\displaystyle R} . Driscoll's zero-one law is a...

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

Compute kernel, in GPGPU programming Kernel method, in machine learning Kernelization, a technique for designing efficient algorithms Kernel, a routine...

In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. This is...

In operator theory, a branch of mathematics, a positive-definite kernel is a generalization of a positive-definite function or a positive-definite matrix...

eBPF tracing tools to provide unique insights into system behavior. As a kernel engineer, he developed the ZFS L2ARC: A pioneering file system performance...

A loadable kernel module (LKM) is an executable library that extends the capabilities of a running kernel, or so-called base kernel, of an operating system...

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...