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Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML, CSS and (optionally) JavaScript -based design templates for typography, forms, buttons, navigation, and other interface components. As of May 2023, Bootstrap is the 17th most starred ...
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 While some parts of BOOTP have been effectively superseded by the Dynamic Host ...
Stage 1: the bootstrap compiler is produced. This compiler is enough to translate its own source into a program which can be executed on the target machine. At this point, all further development is done using the language defined by the bootstrap compiler, and stage 2 begins. Stage 2: a full compiler is produced by the bootstrap compiler.
The Preboot Execution Environment (PXE) was introduced as part of the Wired for Management [2] framework by Intel and is described in the specification published by Intel and SystemSoft. PXE version 2.0 was released in December 1998, and the update 2.1 was made public in September 1999. [3] The PXE environment makes use of several standard ...
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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.
In the field of electronics, a technique where part of the output of a system is used at startup can be described as bootstrapping. A bootstrap circuit is one where part of the output of an amplifier stage is applied to the input, so as to alter the input impedance of the amplifier. When applied deliberately, the intention is usually to ...
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.