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  2. Linear separability - Wikipedia

    en.wikipedia.org/wiki/Linear_separability

    H 1 does not separate the sets. H 2 does, but only with a small margin. H 3 separates them with the maximum margin. Classifying data is a common task in machine learning. Suppose some data points, each belonging to one of two sets, are given and we wish to create a model that will decide which set a new data point will be in.

  3. Perceptron - Wikipedia

    en.wikipedia.org/wiki/Perceptron

    A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. [1] It is a type of linear classifier , i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector .

  4. Kirchberger's theorem - Wikipedia

    en.wikipedia.org/wiki/Kirchberger's_theorem

    Kirchberger's theorem is a theorem in discrete geometry, on linear separability.The two-dimensional version of the theorem states that, if a finite set of red and blue points in the Euclidean plane has the property that, for every four points, there exists a line separating the red and blue points within those four, then there exists a single line separating all the red points from all the ...

  5. Cover's theorem - Wikipedia

    en.wikipedia.org/wiki/Cover's_Theorem

    The left image shows 100 points in the two dimensional real space, labelled according to whether they are inside or outside the circular area. These labelled points are not linearly separable, but lifting them to the three dimensional space with the kernel trick, the points becomes linearly separable. Note that in this case and in many other ...

  6. Linear discriminant analysis - Wikipedia

    en.wikipedia.org/wiki/Linear_discriminant_analysis

    Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or ...

  7. Schwartz space - Wikipedia

    en.wikipedia.org/wiki/Schwartz_space

    The Fourier transform is a linear isomorphism F:𝒮(R n) → 𝒮(R n). If f ∈ 𝒮( R n ) then f is Lipschitz continuous and hence uniformly continuous on R n . 𝒮( R n ) is a distinguished locally convex Fréchet Schwartz TVS over the complex numbers .

  8. Separability - Wikipedia

    en.wikipedia.org/wiki/Separability

    Separable filter, a product of two or more simple filters in image processing; Separable ordinary differential equation, a class of equations that can be separated into a pair of integrals; Separable partial differential equation, a class of equations that can be broken down into differential equations in fewer independent variables

  9. Linear subspace - Wikipedia

    en.wikipedia.org/wiki/Linear_subspace

    If V is a vector space over a field K, a subset W of V is a linear subspace of V if it is a vector space over K for the operations of V.Equivalently, a linear subspace of V is a nonempty subset W such that, whenever w 1, w 2 are elements of W and α, β are elements of K, it follows that αw 1 + βw 2 is in W.