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  2. Linear separability - Wikipedia

    en.wikipedia.org/wiki/Linear_separability

    In Euclidean geometry, linear separability is a property of two sets of points. This is most easily visualized in two dimensions (the Euclidean plane ) by thinking of one set of points as being colored blue and the other set of points as being colored red.

  3. 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 ...

  4. Perceptron - Wikipedia

    en.wikipedia.org/wiki/Perceptron

    Linear separability is testable in time ((/), (), (⁡)), where is the number of data points, and is the dimension of each point. [ 35 ] If the training set is linearly separable, then the perceptron is guaranteed to converge after making finitely many mistakes. [ 36 ]

  5. Separation of variables - Wikipedia

    en.wikipedia.org/wiki/Separation_of_variables

    In mathematics, separation of variables (also known as the Fourier method) is any of several methods for solving ordinary and partial differential equations, in which algebra allows one to rewrite an equation so that each of two variables occurs on a different side of the equation.

  6. Linear interpolation - Wikipedia

    en.wikipedia.org/wiki/Linear_interpolation

    Given the two red points, the blue line is the linear interpolant between the points, and the value y at x may be found by linear interpolation. In mathematics, linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.

  7. Linear subspace - Wikipedia

    en.wikipedia.org/wiki/Linear_subspace

    Let the field be R again, but now let the vector space V be the Cartesian plane R 2. Take W to be the set of points (x, y) of R 2 such that x = y. Then W is a subspace of R 2. Proof: Let p = (p 1, p 2) and q = (q 1, q 2) be elements of W, that is, points in the plane such that p 1 = p 2 and q 1 = q 2.

  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. Bhattacharyya distance - Wikipedia

    en.wikipedia.org/wiki/Bhattacharyya_distance

    A common task in classification is estimating the separability of classes. Up to a multiplicative factor, the squared Mahalanobis distance is a special case of the Bhattacharyya distance when the two classes are normally distributed with the same variances. When two classes have similar means but significantly different variances, the ...