Sentence examples for set of similarities from inspiring English sources

Jack Kerouac's feel for some of the texture of lower-class life and for what he called "the warp of wood of old America" — his appreciation of "the switching moves of boxcars" in Steinbeck, Milestone, and Copland's Of Mice and Men — provided one set of similarities.

The set of similarities within templates is represented as I = { S ( F i, F j ) ∣ F i, F j ∈ T } (6).

A large database and a set of rules for feature selection could then feed a statistical model that describes how unusual the set of similarities between two samples really is, relative to similarities between two randomly selected samples from the population.

Given a set of similarities c ij (e.g. the contact frequency between i and j), find X ∈ R 3 × n such that (3) c i j ≥ c k ℓ ⇔ | | x i − x j | | 2 ≤ | | x k − x ℓ | | 2 A trivial solution of this problem is to set Θ equal to 0. In this case, all points will collapse on the origin.

Using this hypothesis, NMDS attempts to solve the following problem: P roblem 2.1 Given a set of similarities c ij (e.g. the contact frequency between i and j), find X ∈ R 3 × n such that (3) c i j ≥ c k ℓ ⇔ | | x i − x j | | 2 ≤ | | x k − x ℓ | | 2 Equation (3) is known as the non-metric constraint, or the ordinal constraint.

For this, suitable set of similarity transformations utilized.

A set of similarity criteria is proposed to analyze the corresponding relationship between engineering conditions and that in laboratory tests.

The objective of this work is to introduce a set of similarity metrics for comparing surgical process models (SPMs).

Table 2 Set of similarity codes Parameters ϕ 0 Parameters ϕ 0 r = 3 Chu – Px – Frank r = 17 Golomb-Zhang – MPS (Minimum Peak Sidelobe) r = 6,q = 6 Zadoff – P4 – Polyphase Barker – P1 – – Set of similarity codes.

A data object is characterized by a set of similarity or dissimilarity measures which are described by distance function.

In [19], considering a set of similarity measure-based classifiers within the LDA feature space, a sequential search algorithm was applied in order to find an optimum subset of similarity measures to be fused as a basis for decision making.