Sentence examples for be a linearly separable from inspiring English sources

Note that a problem is easy if there exists one attribute for which the ranges of the values spanned by each class do not overlap (in this case, this would be a linearly separable problem).

Our DCA-SVM algorithm's rivaling clinical level performance may suggest that serum proteomic data classification can be a linearly separable problem under appropriate feature selection.

Given P = [ x 1, x 2, ⋯ x N ] T, Q = [ y 1, y 2, ⋯ y M ] T, x i y j ∈ ℜ n, i = 1, 2, ⋯ N, j = 1, 2, ⋯ M, if there exists a hyperplane H: w T v + b = 0, w, v, ∈ ℜ n, b ∈ ℜ, such that ∀ x ∈ P, ∀ y ∈ Q, w T x + b > 0 and w T y + b < 0, then P and Q are linearly separable data, i.e. classifying P and Q is a linearly separable problem.

The main objective of the SVM schemes is to map the input data from the N-dimensional space to the M-dimensional space (M>N), where the classes are supposed to be linearly separable and can be classified by the calculation of a standard separating hyperplane [64], [65] (see Appendix S2 for details on the implementation of SVM).

A linearly separable problem can be simply described as follows.

The dataset essentially holds a linearly separable pattern that can be exploited to classify the actual machine output that happened in a respective learning step.

The effects were assumed to be linearly separable.

Since the data is not guaranteed to be linearly separable (i.e., overlapped), three-layered Backpropagation NN (BPNN) architecture is chosen in this paper as classifier.

It can transform the original data space to a feature space by a nonlinear mapping through kernel methods, where is more likely to be linearly separable than in the original data space.

*It was unnecessary to apply the Gaussian SVM to the one-vs-one case because the training sets were always found to be linearly separable using Ho-Kashyap.

However, the data being classified may not always be linearly separable in the space.