China
Africa
Explore chapters and articles related to this topic
Code Coverage Metrics
Published in Chris Hobbs, Embedded Software Development for Safety-Critical Systems, 2019
A “basis” for a vector space is a set of vectors, none of which can be obtained by taking linear combinations of the others. Most of the tools that generate basis paths hide the mathematics from the user, but if you are interested, see page 355. For generating test cases, the vectors are the potential paths through the program:
Code Coverage Metrics
Published in Chris Hobbs, Embedded Software Development for Safety-Critical Systems, 2017
A “basis” for a vector space is a set of vectors, none of which can be obtained by taking linear combinations of the others. Most of the tools that generate basis paths hide the mathematics from the user, but if you are interested, see page 333. For generating test cases, the vectors are the potential paths through the program:
Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach
Published in Transportation Letters, 2021
José Ángel Martín-Baos, Ricardo García-Ródenas, Luis Rodriguez-Benitez
KLR builds several latent functions, for all , which are equivalent to the systematic utility functions of RUM and, therefore, they are denoted in the same way. Nevertheless, KLR operates with this latent functions as black boxes where the relationship between the feature vector and the utility is not explicitly stated. These latent functions for are searched within function spaces named Reproducing Kernel Hilbert Spaces (RKHS). The RKHS space is a vector space which is univocally generated by the so-called kernel function, and its associated RKHS space is denoted by . The family of functions constitutes a basis of the vector space. Any element from can be represented as a linear combination of basis elements, in particular for . The expression of the latent functions, which from now on will be referred to as utilities, is given by: