distributed kernel - definizione. Che cos'è distributed kernel
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Cosa (chi) è distributed kernel - definizione

CLASS OF ALGORITHMS FOR PATTERN ANALYSIS
Kernel trick; Kernel machine; Kernel Method; Kernel Methods; Kernel machines; Kernel Machines; Kernel methods

Kernel (operating system)         
  • The [[hybrid kernel]] approach combines the speed and simpler design of a monolithic kernel with the modularity and execution safety of a microkernel
  • servers]], separate programs that assume former kernel functions, such as device drivers, GUI servers, etc.
  • A diagram of the predecessor/successor family relationship for [[Unix-like]] systems
MAIN COMPONENT OF MOST COMPUTER OPERATING SYSTEMS
Operating system/kernel; Hybrid monolithic kernel; Operating system kernel; Kernel image; No-kernel; No kernell; No kernel; Kernel (computer); Kernel computer science; Kernel (computer science); Kernel (Computer Science); OS kernel; Kernel memory; Operating system kernels; Kernel design; Nucleus (operating system)
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. It is the portion of the operating system code that is always resident in memory and facilitates interactions between hardware and software components.
Kernel method         
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets.
Kernel (statistics)         
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  • All of the kernels below in a common coordinate system.
TERM IN STATISTICAL ANALYSIS USED TO REFER TO A WINDOW FUNCTION
Window width; Uniform kernel; Triangular kernel; Quartic kernel; Kernel estimation; Epanechnikov kernel; V. A. Epanechnikov; Epanechnikov; V A Epanechnikov; V.A. Epanechnikov; VA Epanechnikov
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.

Wikipedia

Kernel method

In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). Kernel methods are types of algorithms that are used for pattern analysis. These methods involve using linear classifiers to solve nonlinear problems. The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets. For many algorithms that solve these tasks, the data in raw representation have to be explicitly transformed into feature vector representations via a user-specified feature map: in contrast, kernel methods require only a user-specified kernel, i.e., a similarity function over all pairs of data points computed using inner products. The feature map in kernel machines is infinite dimensional but only requires a finite dimensional matrix from user-input according to the Representer theorem. Kernel machines are slow to compute for datasets larger than a couple of thousand examples without parallel processing.

Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often computationally cheaper than the explicit computation of the coordinates. This approach is called the "kernel trick". Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors.

Algorithms capable of operating with kernels include the kernel perceptron, support-vector machines (SVM), Gaussian processes, principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters and many others.

Most kernel algorithms are based on convex optimization or eigenproblems and are statistically well-founded. Typically, their statistical properties are analyzed using statistical learning theory (for example, using Rademacher complexity).