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A geometric modeling kernel is a solid modeling software component used in computer-aided design (CAD) packages. [ 1 ] [ 2 ] Available modelling kernels include: ACIS is developed and licensed by Spatial Corporation of Dassault Systèmes .
The initial CGM implementation was effectively a streamed representation of a sequence of Graphical Kernel System (GKS) primitive operations. It has been adopted to some extent in the areas of technical illustration and professional design , but has largely been superseded by formats such as SVG and DXF .
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 ...
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.
The 3D ACIS Modeler (ACIS) is a geometric modeling kernel developed by Spatial Corporation (formerly Spatial Technology), part of Dassault Systèmes.ACIS is used by software developers in industries such as computer-aided design, computer-aided manufacturing, computer-aided engineering, architecture, engineering and construction, coordinate-measuring machine, 3D animation, and shipbuilding.
The product was first released by DInsight in 2001 under Kernel CAD name. In version 6.0, released in December 2018, the main product was renamed to DG Kernel. The most significant change in version 6 was an alternative high-level interface for OCCT technology, which solves a number of issues with using OCCT directly.
The kernel of a reproducing kernel Hilbert space is used in the suite of techniques known as kernel methods to perform tasks such as statistical classification, regression analysis, and cluster analysis on data in an implicit space. This usage is particularly common in machine learning.
Output after kernel PCA, with a Gaussian kernel. Note in particular that the first principal component is enough to distinguish the three different groups, which is impossible using only linear PCA, because linear PCA operates only in the given (in this case two-dimensional) space, in which these concentric point clouds are not linearly separable.