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CROSSWORD
ANSWER

ransac

Searching in Crosswords ...

The answer RANSAC has 0 possible clue(s) in existing crosswords.

Searching in Word Games ...

The word RANSAC is NOT valid in any word game. (Sorry, you cannot play RANSAC in Scrabble, Words With Friends etc)

There are 6 letters in RANSAC ( A1C3N1R1S1 )

To search all scrabble anagrams of RANSAC, to go: RANSAC

Rearrange the letters in RANSAC and see some winning combinations

Scrabble results that can be created with an extra letter added to RANSAC

ACRASIN ARNICAS CANARDS CARINAS RANSACK SARCINA ANARCHS

5 letters out of RANSAC

CARNSNARCSSACRASARAN

4 letters out of RANSAC

ANASANSAARCSCANSCARNCARSCASANARCSCANSCAR

3 letters out of RANSAC

AASANAARCARSCANCARRANRASSAC

2 letters out of RANSAC

AAANARASNA

Searching in Dictionaries ...

Definitions of ransac in various dictionaries:

RANSAC - Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outl...

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Keep reading for additional results and analysis below.

Ransac description
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed. The algorithm was first published by Fischler and Bolles at SRI International in 1981. They used RANSAC to solve the Location Determination Problem (LDP), where the goal is to determine the points in the space that project onto an image into a set of landmarks with known locations.
* A basic assumption is that the data consists of "inliers", i.e., data whose distribution can be explained by some set of model parameters, though may be subject to noise, and "outliers" which are data that do not fit the model.
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