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Huffman tree generated from the exact frequencies of the text "this is an example of a huffman tree". Encoding the sentence with this code requires 135 (or 147) bits, as opposed to 288 (or 180) bits if 36 characters of 8 (or 5) bits were used (This assumes that the code tree structure is known to the decoder and thus does not need to be counted as part of the transmitted information).
The Luhn mod N algorithm generates a check digit (more precisely, a check character) within the same range of valid characters as the input string. For example, if the algorithm is applied to a string of lower-case letters (a to z), the check character will also be a lower-case letter.
The California Job Case was a compartmentalized box for printing in the 19th century, sizes corresponding to the commonality of letters. The frequency of letters in text has been studied for use in cryptanalysis, and frequency analysis in particular, dating back to the Arab mathematician al-Kindi (c. AD 801–873 ), who formally developed the method (the ciphers breakable by this technique go ...
Run-length encoding compresses data by reducing the physical size of a repeating string of characters. This process involves converting the input data into a compressed format by identifying and counting consecutive occurrences of each character. The steps are as follows: Traverse the input data.
Figure 1 shows several example sequences and the corresponding 1-gram, 2-gram and 3-gram sequences. Here are further examples; these are word-level 3-grams and 4-grams (and counts of the number of times they appeared) from the Google n-gram corpus. [4] 3-grams ceramics collectables collectibles (55) ceramics collectables fine (130)
The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision .
The ASCII text-encoding standard uses 7 bits to encode characters. With this it is possible to encode 128 (i.e. 2 7) unique values (0–127) to represent the alphabetic, numeric, and punctuation characters commonly used in English, plus a selection of Control characters which do not represent printable characters.
A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words.A bigram is an n-gram for n=2.. The frequency distribution of every bigram in a string is commonly used for simple statistical analysis of text in many applications, including in computational linguistics, cryptography, and speech recognition.