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https://www.vector.com/int/en/products/products-a-z/software/vectorcast/
The VectorCAST embedded software testing platform is a family of products that automates testing activities across the software development lifecycle. The purpose of this section is to provide a high-level view of what the VectorCAST products do, how they interwork, and how you might best use them to fully meet your own software testing needs.
https://www.vector.com/int/en/products/products-a-z/software/ta-tool-suite/
The modular TA Tool Suite provides a dedicated software product for each step of the software project lifecycle. This includes specification of timing requirements, analysis and optimization of software architecture design as well as verification of the target system behavior.
https://en.wikipedia.org/wiki/Vector_optimization
Vector optimization is a subarea of mathematical optimization where optimization problems with a vector-valued objective functions are optimized with respect to a given partial ordering and subject to certain constraints. A multi-objective optimization problem is a special case of a vector optimization problem: The objective space is the finite dimensional Euclidean space partially ordered by ...
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://jeremykun.com/2017/06/05/formulating-the-support-vector-machine-optimization-problem/
Jun 05, 2017 · Formulating the Support Vector Machine Optimization Problem. Posted on June 5, 2017 by j2kun. The hypothesis and the setup. This blog post has an interactive demo (mostly used toward the end of the post). The source for this demo is available in a Github repository.
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
derivation of the analytic optimization. 1.1 Overview of Support Vector Machines Vladimir Vapnik invented Support Vector Machines in 1979 [19]. In its simplest, linear form, an SVM is a hyperplane that separates a set of positive examples from a set of negative examples with maximum margin (see figure 1).Cited by: 3108
https://assets.vector.com/cms/content/products/asap2/Docs/ASAP2Tool-Set_Manual_EN.pdf
Vector disclaims all liabilities for the completeness or correctness of the contents and for damages which may result from the use of this documentation. 1.2.2 Support
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