Learning Macros with an Enhanced LZ78 Algorithm

Forrest Elliott and Manfred Huber, The University of Texas at Arlington

One application of the Lempel-Ziv LZ78 algorithm, other than compression, is learning repeating sequences in a data stream One shortcoming of the algorithm though is its slow learning rate. In this paper we enhance the algorithm for improved performance from a learning perspective and apply it to the learning of user macros in a computer desktop environment. Once a macro is learned it can be predicted and offered back at opportune times. With the enhanced algorithm, it is possible for a macro to be learned in as few as two exposures to a sequence.

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