Sentence examples for space of the class from inspiring English sources

(parameter space of the class (mathcal {L}(mathbf {x};mathbf {a})) ).

A soft margin SVM uses a slack variable ξ and can tolerate training data points that, e.g. clearly lie in the space of the class they do not belong to without skewing the separating hyperplane in order to correctly classify such outliers.

It has been shown by Edwards et al. [ 24] and by Steipe and Kaindl [ 25, 26] that RMSD opt is a metric on the space of the classes of equivalent structures (i. e. structures that can be superimposed exactly by a rototranslation).

Theorem 3.18 and Corollary 3.19 generalize Theorem 2.6 in [12] from the class of convex spaces to the class of abstract convex spaces under the weaker assumptions.

The constant bounding this calculus grows as logσeε as σε→∞ and this growth is sharp over all Banach space operators of the class under consideration.

Then (mathcal{T}(H)), the space of the trace class operators on H, has the weak ∗-FPP for left reversible groups, where the predual is (E=K(H)), the space of all compact operators defined on H.

They were all trespassers in the sense that they crossed the boundary of rigid social-class demarcations and occupied the space of the lower classes.

N-LDA aims to maximize the between-class distance in the null space of the within-class scatter matrix, while OLDA computes a set of orthogonal discriminant vectors via the simultaneous diagonalization of the scatter matrices.

In 1967, Borsuk (see, [1]) introduced the notion of the domination of space in the class of metric spaces, i.e., a metric space Y dominates a metric space X (we write Y ≥ X) if there are continuous maps f :Y → X, g : X → Y such that for every x ∈ X f(g(x)) = x.

This process starts by calculating the projection vectors in the null space of the within-class scatter matrix Sw.

By and,, we denote the space of continuous, real-value functions and the space of (the equivalence classes of) -integrable functions defined on endowed with the respective norms (1.3).