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The implementation or design is regarded as a model of the system, whereas the specifications are properties that the model must satisfy. [2] An important class of model-checking methods has been developed for checking models of hardware and software designs where the specification is given by a temporal logic formula.
In 1999, Judi Romijn compared two model checkers (CADP and SPIN) on the HAVi interoperability audio-video protocol for consumer electronics. [3] In 2003, Yifei Dong, Xiaoqun Du, Gerard J. Holzmann, and Scott A. Smolka published a comparison of four model checkers (namely: Cospan, Murphi, SPIN, and XMC) on a communication protocol, the GNU i ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston (1997) [7] only two modeling methodologies stand out, top-down and bottom-up: Bottom-up models or View Integration models are often the result of a reengineering effort. They usually start with existing data ...
De facto standard for matrix/tensor operations in Python. Pandas, a library for data manipulation and analysis. SageMath is a large mathematical software application which integrates the work of nearly 100 free software projects and supports linear algebra, combinatorics, numerical mathematics, calculus, and more. [17]
By Katie Paul. NEW YORK - Facebook owner Meta said on Friday it was releasing a batch of new AI models from its research division, including a "Self-Taught Evaluator" that may offer a path toward ...
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis.In particular, it offers data structures and operations for manipulating numerical tables and time series.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...