When.com Web Search

  1. Ads

    related to: linearly separable patterns of multiplication practice

Search results

  1. Results From The WOW.Com Content Network
  2. Linear separability - Wikipedia

    en.wikipedia.org/wiki/Linear_separability

    The existence of a line separating the two types of points means that the data is linearly separable 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 ...

  3. 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 ...

  4. Perceptron - Wikipedia

    en.wikipedia.org/wiki/Perceptron

    This enabled the perceptron to classify analogue patterns, by projecting them into a binary space. In fact, for a projection space of sufficiently high dimension, patterns can become linearly separable. Another way to solve nonlinear problems without using multiple layers is to use higher order networks (sigma-pi unit).

  5. Schauder basis - Wikipedia

    en.wikipedia.org/wiki/Schauder_basis

    An uncountable Schauder basis is a linearly ordered set rather than a sequence, and each sum inherits the order of its terms from this linear ordering. They can and do arise in practice. As an example, a separable Hilbert space can only have a countable Schauder basis, but a non-separable Hilbert space may have an uncountable one.

  6. Multi-surface method - Wikipedia

    en.wikipedia.org/wiki/Multi-surface_method

    Two datasets are linearly separable if their convex hulls do not intersect. The method may be formulated as a feedforward neural network with weights that are trained via linear programming . Comparisons between neural networks trained with the MSM versus backpropagation show MSM is better able to classify data. [ 1 ]

  7. 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 ...