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An embedded system on a plug-in card with processor, memory, power supply, and external interfaces. An embedded system is a specialized computer system—a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electronic system.
The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [ 1 ] [ 3 ] [ 5 ] [ 6 ] [ 7 ] [ 18 ] Rossi et al. [ 5 ] produced an extensive benchmark of the models, but also other surveys produces similar results.
Embedded software is computer software, written to control machines or devices that are not typically thought of as computers, commonly known as embedded systems. It is typically specialized for the particular hardware that it runs on and has time and memory constraints. [1] This term is sometimes used interchangeably with firmware. [2]
In general topology, an embedding is a homeomorphism onto its image. [3] More explicitly, an injective continuous map : between topological spaces and is a topological embedding if yields a homeomorphism between and () (where () carries the subspace topology inherited from ).
Embedded method for Feature selection. Embedded methods have been recently proposed that try to combine the advantages of both previous methods. A learning algorithm takes advantage of its own variable selection process and performs feature selection and classification simultaneously, such as the FRMT algorithm. [49]
Latent spaces are usually fit via machine learning, and they can then be used as feature spaces in machine learning models, including classifiers and other supervised predictors. The interpretation of the latent spaces of machine learning models is an active field of study, but latent space interpretation is difficult to achieve.