除了它在软件开发中的应用(例如kdgregory给出的答案)和Dirk Eddelbuettel所讨论的在统计学中的应用之外，在强化学习的上下文中，Bootstrapping还有另一个对开发人员有用的含义。

来自萨顿和巴托:

Widrow, Gupta, and Maitra (1973) modified the Least-Mean-Square (LMS) algorithm of Widrow and Hoff (1960) to produce a reinforcement learning rule that could learn from success and failure signals instead of from training examples. They called this form of learning “selective bootstrap adaptation” and described it as “learning with a critic” instead of “learning with a teacher.” They analyzed this rule and showed how it could learn to play blackjack. This was an isolated foray into reinforcement learning by Widrow, whose contributions to supervised learning were much more influential.

这本书描述了各种强化算法，其中目标值是基于先前的近似作为bootstrap方法:

最后，我们注意到 DP[动态规划]方法的最后一个特殊性质。所有这些都更新了估计 基于对继承者价值的估计的状态值 州。也就是说，他们在其他数据的基础上更新估计 估计。我们把这叫做引导。许多钢筋 学习方法执行引导，即使是那些没有引导的方法 要求，正如DP要求，一个完整和准确的模型 环境。

请注意，这不同于维基百科页面上提到的自举聚合和智能爆炸。

在应用程序开发的上下文中，“引导”通常在讨论模块化和/或自动更新软件时出现。

而不是用户下载整个应用程序，包括他不需要的功能，并重新下载和手动更新它，只要有更新，用户只下载和启动一个小的“引导”可执行文件，它反过来下载和安装用户需要的应用程序的那些部分。此外，引导组件能够在每次启动时查找更新并安装它们。

为了完整起见，它也是统计学中一个相当重要的(相对较新的)方法，它使用重采样/模拟从样本中推断总体属性。它在维基百科上有一篇关于自我引导(统计)的长篇文章。

在一些web框架中有一个引导的例子。你调用index.php (bootstrapper)，然后它加载框架的助手、模型、配置，然后加载控制器并将控制权传递给它。

正如您所看到的，这是一个启动大进程的简单文件。

As a humble beginner in the world of programming, and flicking through all the answers here after seeing this word used a lot in apparently slightly different ways in different places, I found reading the Wikipedia page on Bootstrapping (duh! I didn't think of it either at first) is very informative to understand differences in use of this word. Could it be......on extremely rare occasions......Wikipedia might even have better explanations of certain terms than....(redacted)? Will they bring in rep points on Wikipedia though?

对我来说，似乎所有的意义都与:从尽可能简单的东西开始，用Thing2做一些稍微复杂的东西，现在你可以用Thing2比原来的Thing1更有效、更快地完成某些任务。然后从第二件事重复到第三件事，直到无限……

I see it as closely connected to both biological evolution and 'Layers of Abstraction' (newbies like me see, ahem, Wikipedia, cough) - the evolution from 1940's computers with switches, machine code, Assembly, C, Python, AIs you can give all kinds of complex instructions to like "make the %4^% dinner to my default &^$% requirements and clean the floor you %$£"@:~" in drunken slang English or Amazon tribal dialect without them 'raising an exception' (for newbies again...you guessed it) - missed out lot of links there due to simple ignorance.

那么在某些特定的软件意义上: 含义1:Thing1用于加载Thing2的最新版本(因为Thing2当然会比Thing1大，就像Thing3会比Thing2大一样)。

Meaning2: Thing1 is a lower level language (closer to 1001011100....011001 than print("Hello, ", user.name)) used to write a little bit of the higher language of Thing2, then this little bit of Thing2 is used to expand Thing2 itself from baby vocabulary level towards adult vocabulary level (Thing2 starts to be processed, or to use correct technical term 'compiled', by the baby version of itself (it's a clever baby!), whereas the baby version of Thing2 itself could of course only be compiled by Thing1, cause it can't exist before it exists, right duh!), then child version of Thing2 compiles Surly Teenager version of Thing2, at which point programming community decides whether Surly Teenager's 'issues' (software term and metaphor term!) are worth spending enough time resolving to be accepted long term, or to abandon them to (not sure where to take the analogy here).

如果是，那么Thing2已经“bootstrap”自己(可能有几次)从婴儿到成年:“孩子是男人的父亲”(华兹华斯，建议不要试图在Stack Overflow上查找引用或作者)。

除了它在软件开发中的应用(例如kdgregory给出的答案)和Dirk Eddelbuettel所讨论的在统计学中的应用之外，在强化学习的上下文中，Bootstrapping还有另一个对开发人员有用的含义。

来自萨顿和巴托:

Widrow, Gupta, and Maitra (1973) modified the Least-Mean-Square (LMS) algorithm of Widrow and Hoff (1960) to produce a reinforcement learning rule that could learn from success and failure signals instead of from training examples. They called this form of learning “selective bootstrap adaptation” and described it as “learning with a critic” instead of “learning with a teacher.” They analyzed this rule and showed how it could learn to play blackjack. This was an isolated foray into reinforcement learning by Widrow, whose contributions to supervised learning were much more influential.

这本书描述了各种强化算法，其中目标值是基于先前的近似作为bootstrap方法:

最后，我们注意到 DP[动态规划]方法的最后一个特殊性质。所有这些都更新了估计 基于对继承者价值的估计的状态值 州。也就是说，他们在其他数据的基础上更新估计 估计。我们把这叫做引导。许多钢筋 学习方法执行引导，即使是那些没有引导的方法 要求，正如DP要求，一个完整和准确的模型 环境。

请注意，这不同于维基百科页面上提到的自举聚合和智能爆炸。