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  2. Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Huffman_coding

    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).

  3. File:Huffman coding example.svg - Wikipedia

    en.wikipedia.org/.../File:Huffman_coding_example.svg

    The standard way to represent a signal made of 4 symbols is by using 2 bits/symbol, but the entropy of the source is 1.73 bits/symbol. If this Huffman code is used to represent the signal, then the entropy is lowered to 1.83 bits/symbol; it is still far from the theoretical limit because the probabilities of the symbols are different from negative powers of two.

  4. Canonical Huffman code - Wikipedia

    en.wikipedia.org/wiki/Canonical_Huffman_code

    The normal Huffman coding algorithm assigns a variable length code to every symbol in the alphabet. More frequently used symbols will be assigned a shorter code. For example, suppose we have the following non-canonical codebook: A = 11 B = 0 C = 101 D = 100 Here the letter A has been assigned 2 bits, B has 1 bit, and C and D both have 3 bits.

  5. Inductive probability - Wikipedia

    en.wikipedia.org/wiki/Inductive_probability

    A Huffman code must distinguish the 3 cases. The length of each code is based on the frequency of each type of sub expressions. Initially constants are all assigned the same length/probability. Later constants may be assigned a probability using the Huffman code based on the number of uses of the function id in all expressions recorded so far.

  6. File:Huffman tree 2.svg - Wikipedia

    en.wikipedia.org/wiki/File:Huffman_tree_2.svg

    Date/Time Thumbnail Dimensions User Comment; current: 18:43, 7 October 2007: 625 × 402 (68 KB): Meteficha {{Information |Description=Huffman tree generated from the exact frequencies in the sentence "this is an example of a huffman tree".

  7. Shannon–Fano coding - Wikipedia

    en.wikipedia.org/wiki/Shannon–Fano_coding

    This is a constraint that is often unneeded, since the codes will be packed end-to-end in long sequences. If we consider groups of codes at a time, symbol-by-symbol Huffman coding is only optimal if the probabilities of the symbols are independent and are some power of a half, i.e., /.

  8. HuffPost Data

    data.huffingtonpost.com

    Interactive charts showing the $10 billion divide between elite college sports programs and all the rest. Sports At Any Cost A HuffPost investigation into how college students are bankrolling the athletics arms race.

  9. Package-merge algorithm - Wikipedia

    en.wikipedia.org/wiki/Package-merge_algorithm

    The package-merge algorithm is an O(nL)-time algorithm for finding an optimal length-limited Huffman code for a given distribution on a given alphabet of size n, where no code word is longer than L. It is a greedy algorithm, and a generalization of Huffman's original algorithm.