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|Title:||INDUCTIVE MODELING FOR DATA COMPRESSION|
|Authors:||Cleary, John G.|
Witten, Ian H.
|Abstract:||The modern paradigm for data compression is modeling followed by coding. Within this paradigm the problem of optimal coding with respect to a model is fully solved. Inductive modeling techniques used for compression operate despite considerable noise, employ positive examples only, and yet effectively identify sources of the kind that are encountered in practice. The problem of data compression provides an excellent test-bed for inductive modeling. It allows one to assess the merits of adaptive as opposed to non-adaptive approaches. For example, it can be shown that the penalty incurred by adaptation is less than the cost of transmitting an explicit model for the source. It also illustrates that modeling criteria such as "identification in the limit" may be inappropriate because typical sources have time-varying characteristics. In practice speed of convergence is just as important as asymptotic performance.|
|Appears in Collections:||Cleary, John |
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