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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. Lp space - Wikipedia

    en.wikipedia.org/wiki/Lp_space

    Two cosets are equal + = + if and only if + (or equivalently, ), which happens if and only if = almost everywhere; if this is the case then and are identified in the quotient space. Hence, strictly speaking L p ( S , μ ) {\displaystyle L^{p}(S,\,\mu )} consists of equivalence classes of functions.

  5. 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 ]

  6. 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.

  7. Multidimensional discrete convolution - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_discrete...

    The image itself is not separable. If the result is calculated using the direct convolution approach without exploiting the separability of the filter, this will require approximately multiplications and additions. If the separability of the filter is taken into account, the filtering can be performed in two steps.

  8. 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

  9. 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 ...