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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. 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.

  5. Separability - Wikipedia

    en.wikipedia.org/wiki/Separability

    Linear separability, a geometric property of a pair of sets of points in Euclidean geometry; Recursively inseparable sets, in computability theory, pairs of sets of natural numbers that cannot be "separated" with a recursive set

  6. Affine transformation - Wikipedia

    en.wikipedia.org/wiki/Affine_transformation

    Let X be an affine space over a field k, and V be its associated vector space. An affine transformation is a bijection f from X onto itself that is an affine map; this means that a linear map g from V to V is well defined by the equation () = (); here, as usual, the subtraction of two points denotes the free vector from the second point to the first one, and "well-defined" means that ...

  7. Linear discriminant analysis - Wikipedia

    en.wikipedia.org/wiki/Linear_discriminant_analysis

    Linear classification in this non-linear space is then equivalent to non-linear classification in the original space. The most commonly used example of this is the kernel Fisher discriminant . LDA can be generalized to multiple discriminant analysis , where c becomes a categorical variable with N possible states, instead of only two.

  8. Peres–Horodecki criterion - Wikipedia

    en.wikipedia.org/wiki/Peres–Horodecki_criterion

    The Peres–Horodecki criterion is a necessary condition, for the joint density matrix of two quantum mechanical systems and , to be separable. It is also called the PPT criterion, for positive partial transpose. In the 2×2 and 2×3 dimensional cases the condition is also sufficient.

  9. Range criterion - Wikipedia

    en.wikipedia.org/wiki/Range_criterion

    Thus Ran(M) coincides with the linear span of {}. The range criterion is a special case of this fact. The range criterion is a special case of this fact. A density matrix ρ acting on H is separable if and only if it can be written as