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  2. Kernel regression - Wikipedia

    en.wikipedia.org/wiki/Kernel_regression

    MATLAB: A free MATLAB toolbox with implementation of kernel regression, kernel density estimation, kernel estimation of hazard function and many others is available on these pages (this toolbox is a part of the book [6]).

  3. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    A free MATLAB toolbox with implementation of kernel regression, kernel density estimation, kernel estimation of hazard function and many others is available on these pages (this toolbox is a part of the book [30]).

  4. Kernel (statistics) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(statistics)

    In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in kernel density estimation to estimate random variables' density functions, or in kernel regression to estimate the conditional expectation of a random variable.

  5. Multivariate kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Multivariate_kernel...

    kde2d.m A Matlab function for bivariate kernel density estimation. libagf A C++ library for multivariate, variable bandwidth kernel density estimation. akde.m A Matlab m-file for multivariate, variable bandwidth kernel density estimation. helit and pyqt_fit.kde Module in the PyQt-Fit package are Python libraries for multivariate kernel density ...

  6. Nonparametric regression - Wikipedia

    en.wikipedia.org/wiki/Nonparametric_regression

    Kernel regression estimates the continuous dependent variable from a limited set of data points by convolving the data points' locations with a kernel function—approximately speaking, the kernel function specifies how to "blur" the influence of the data points so that their values can be used to predict the value for nearby locations.

  7. Matérn covariance function - Wikipedia

    en.wikipedia.org/wiki/Matérn_covariance_function

    In statistics, the Matérn covariance, also called the Matérn kernel, [1] is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and other applications of multivariate statistical analysis on metric spaces. It is named after the Swedish forestry statistician Bertil Matérn. [2]

  8. Kernel principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Kernel_principal_component...

    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.

  9. Radial basis function kernel - Wikipedia

    en.wikipedia.org/wiki/Radial_basis_function_kernel

    Since the value of the RBF kernel decreases with distance and ranges between zero (in the infinite-distance limit) and one (when x = x'), it has a ready interpretation as a similarity measure. [2] The feature space of the kernel has an infinite number of dimensions; for =, its expansion using the multinomial theorem is: [3]