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  2. Kernel (linear algebra) - Wikipedia

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

    The left null space of A is the same as the kernel of A T. The left null space of A is the orthogonal complement to the column space of A, and is dual to the cokernel of the associated linear transformation. The kernel, the row space, the column space, and the left null space of A are the four fundamental subspaces associated with the matrix A.

  3. Kernel methods for vector output - Wikipedia

    en.wikipedia.org/wiki/Kernel_methods_for_vector...

    The estimator of the vector-valued regularization framework can also be derived from a Bayesian viewpoint using Gaussian process methods in the case of a finite dimensional Reproducing kernel Hilbert space. The derivation is similar to the scalar-valued case Bayesian interpretation of regularization.

  4. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    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 ...

  5. Kernel (algebra) - Wikipedia

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

    The kernel of a matrix, also called the null space, is the kernel of the linear map defined by the matrix. The kernel of a homomorphism is reduced to 0 (or 1) if and only if the homomorphism is injective, that is if the inverse image of every element consists of a single element. This means that the kernel can be viewed as a measure of the ...

  6. Polynomial kernel - Wikipedia

    en.wikipedia.org/wiki/Polynomial_kernel

    The hyperplane learned in feature space by an SVM is an ellipse in the input space. In machine learning , the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original ...

  7. Geometric modeling kernel - Wikipedia

    en.wikipedia.org/wiki/Geometric_modeling_kernel

    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 .

  8. Kernel principal component analysis - Wikipedia

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

    In the field of multivariate statistics, kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space .

  9. Math Kernel Library - Wikipedia

    en.wikipedia.org/wiki/Math_Kernel_Library

    Intel oneAPI Math Kernel Library (Intel oneMKL) , formerly known as Intel Math Kernel Library, is a library of optimized math routines for science, engineering, and financial applications. Core math functions include BLAS , LAPACK , ScaLAPACK , sparse solvers, fast Fourier transforms , and vector math.