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According to Shannon's Source Coding Theorem , the optimal code length for a symbol is −log''bP'', where ''b'' is the number of symbols used to make output codes and ''P'' is the probability of the input symbol. Two of the most common entropy encoding techniques are Huffman Coding and Arithmetic Coding . If the approximate entropy characteristics of a data stream are known in advance (especially for Signal Compression ), a simpler static code may be useful. These static codes include Universal Codes (such as Elias Gamma Coding or Fibonacci Coding ) and Golomb Codes (such as Unary Coding or Rice Coding ). ENTROPY AS A MEASURE OF SIMILARITY Besides using entropy encoding as a way to compress (and losslessly recover) digital data, an entropy encoder can also be used to measure the amount of similarity between streams of data. This is done by generating an entropy coder/compressor for each class of data; unknown data is then classified by feeding the uncompressed data to each compressor and seeing which compressor yields the highest compression. The coder with the best compression is probably the coder trained on the data that was most similar to the unknown data. EXTERNAL LINKS
''An earlier (open content) version of the above article was posted on PlanetMath .'' |
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