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...

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...

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

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

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

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

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...

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

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...

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

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

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...

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

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

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

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...

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

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

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

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...

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...

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

learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the...

In mathematics, the kernel of a linear map, also known as the null space or nullspace, is the part of the domain which is mapped to the zero vector of...

dimensional subspace. In machine learning, kernel functions are often represented as Gram matrices. (Also see kernel PCA) Since the Gram matrix over the reals...

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...