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Hamming Distance
4 Types of Distance Metrics in Machine Learning Euclidean Distance. Manhattan Distance. Minkowski Distance. Hamming Distance.
The answer is hamming. Check out this tutorial on SimilarityMeasurments: http://people.revoledu.com/kardi/tutorial/Similarity/index.html
The minimum distance of a code outlet, often referred to in the context of coding theory, is the smallest Hamming distance between any two distinct codewords in a code. It indicates the error-detecting and error-correcting capabilities of the code: a minimum distance of (d) allows the detection of up to (d-1) errors and correction of up to (\lfloor (d-1)/2 \rfloor) errors. This concept is crucial in designing reliable communication systems.
In comparing two bit patterns, the Hamming distance is the count of bits different in the two patterns. More generally, if two ordered lists of items are compared, the Hamming distance is the number of items that do not identically agree. This distance is applicable to encoded information, and is a particularly simple metric of comparison, often more useful than the city-block distance (the sum of absolute values of distances along the coordinate axes) or Euclidean distance (the square root of the sum of squares of the distances along the coordinate axes). also Metric.
Hamming code is a linear error-correcting code named after its inventor, Richard Hamming. Hamming codes can detect and correct single-bit errors, and can detect (but not correct) double-bit errors. In other words, the Hamming distance between the transmitted and received code-words must be zero or one for reliable communication.
Ronald Hamming was born in 1973.
Richard Hamming was born on 1915-02-11.
Richard Hamming died on 1998-01-07.