Metrics for Symbol Clustering from a Pseudoergodic Information Source
نویسندگان
چکیده
We discuss a set of metrics, which aims to facilitate the formation of symbol groups from a pseudoergodic information source. An optimal codification can then be applied on the symbols(such as Huffman Codes [1]) for zero memory sources where it tends to the theorical limit of compression limited by the entropy. These metrics can be used as a fitness measure of the individuals in the Vasconcelos genetic algorithm as an alternative to exhaustive search.
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تاریخ انتشار 2003