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Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy ( bias, variance, confidence intervals, prediction error, etc.) to sample estimates.
Generic Bootstrapping Architecture. Generic Bootstrapping Architecture ( GBA) is a technology that enables the authentication of a user. This authentication is possible if the user owns a valid identity on an HLR ( Home Location Register) or on an HSS ( Home Subscriber Server ).
Wikipedia
Bootstrapping (compilers) In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler (or assembler) written in the source programming language that it intends to compile. An initial core version of the compiler (the bootstrap compiler) is generated in a different language (which could be ...
The disk, tape or card deck must contain a special program to load the actual operating system or standalone utility into main storage, and for this specific purpose "IPL Text" is placed on the disk by the stand-alone DASDI (Direct Access Storage Device Initialization) program or an equivalent program running under an operating system, e.g ...
t. e. The Bootstrap Protocol ( BOOTP) is a computer networking protocol used in Internet Protocol networks to automatically assign an IP address to network devices from a configuration server. The BOOTP was originally defined in RFC 951 published in 1985. While some parts of BOOTP have been effectively superseded by the Dynamic Host ...
In computer technology, the term bootstrapping refers to language compilers that are able to be coded in the same language. (For example, a C compiler is now written in the C language. Once the basic compiler is written, improvements can be iteratively made, thus pulling the language up by its bootstraps).
t. e. Bootstrap aggregating, also called bagging (from b ootstrap agg regat ing ), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting.