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Thus, the existence of duplicates does not affect the value of the extreme order statistics. There are other estimation techniques other than min/max sketches. The first paper on count-distinct estimation [7] describes the Flajolet–Martin algorithm, a bit pattern sketch. In this case, the elements are hashed into a bit vector and the sketch ...
Toggle selected state of focused checkbox, radio button, or toggle button Space: Space: Space: Space: Activate focused button, menu item etc. ↵ Enter: Space (also ↵ Enter [notes 5] for menu items) ↵ Enter: ↵ Enter: Expand a drop-down list F4 or Alt+↓: Select/move to first/last item in selected widget Home / End: Home / End
HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. [1] Calculating the exact cardinality of the distinct elements of a multiset requires an amount of memory proportional to the cardinality, which is impractical for very large data sets. Probabilistic cardinality estimators ...
page-info-kbd-shortcut [6] – The "I" keyboard shortcut now opens the "Page information" link in your sidebar. superjump [7] – Custom keyboard shortcuts to go to any page. accessKeysCheatSheet [8] - The "?" keyboard shortcut now overlays a list of all keyboard shortcuts available on the current page.
In computing, a keyboard shortcut (also hotkey/hot key or key binding) [1] is a software-based assignment of an action to one or more keys on a computer keyboard. Most operating systems and applications come with a default set of keyboard shortcuts , some of which may be modified by the user in the settings .
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A common solution is to combine both the mean and the median: Create hash functions and split them into distinct groups (each of size ). Within each group use the mean for aggregating together the l {\displaystyle l} results, and finally take the median of the k {\displaystyle k} group estimates as the final estimate.
which shows which documents contain which terms and how many times they appear. Note that, unlike representing a document as just a token-count list, the document-term matrix includes all terms in the corpus (i.e. the corpus vocabulary), which is why there are zero-counts for terms in the corpus which do not also occur in a specific document.