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Set 3-1 has three possible versions: [0 1 1 1 2 T], [0 1 1 T E 1], and [0 T T 1 E 1], where the subscripts indicate adjacency intervals. The normal form is the smallest "slice of pie" (shaded) or most compact form; in this case, [0 1 1 1 2 T]. This is a list of set classes, by Forte number. [1]
The interface renders a div with one of the "mw-" classes around messages like MediaWiki:Protectedpagewarning. MediaWiki:Common.css: MediaWiki:Editingold, includes/EditPage.php: firstHeading Used on the title of the page, e.g. "Wikipedia:Catalogue of CSS classes" monobook/main.css (screen, projection) skins/MonoBook.php: floatright, floatleft ...
Bootstrap 3 features new plugin system with namespaced events. Bootstrap 3 dropped Internet Explorer 7 and Firefox 3.6 support, but there is an optional polyfill for these browsers. [13] Bootstrap 3 was also the first version released under the twbs organization on GitHub instead of the Twitter one. [14]
Bootstrap materials reinforce core concepts from mainstream subjects like Math, Physics and more, enabling non-CS teachers to adopt the introductory materials while delivering rigorous and engaging computing content drawn from Computer Science classes at universities like Brown, WPI, and Northeastern.
[2] [3] There is a well-known myth about the word quiz that says that in 1791, a Dublin theatre owner named Richard Daly made a bet that he could introduce a word into the language within 24 hours. He then went out and hired a group of street children to write the word "quiz", which was a nonsense word, on walls around the city of Dublin.
[2]: 182 [note 1] In pseudocode, the training algorithm for an OvR learner constructed from a binary classification learner L is as follows: Inputs: L, a learner (training algorithm for binary classifiers) samples X; labels y where y i ∈ {1, … K} is the label for the sample X i; Output: a list of classifiers f k for k ∈ {1, …, K} Procedure:
[clarification needed] [3] BEST is based on a population, P, relative to some hyperspace, R, that represents the universe of possible samples. P * is the realized values of P based on a calibration set, T. T is used to find all possible variation in P. P * is bound by parameters C and B. C is the expectation value of P, written E(P), and B is a ...
If ′ =, then for large the set is expected to have the fraction (1 - 1/e) (~63.2%) of the unique samples of , the rest being duplicates. [1] This kind of sample is known as a bootstrap sample. Sampling with replacement ensures each bootstrap is independent from its peers, as it does not depend on previous chosen samples when sampling.