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High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of over fitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric...
Many applications such as telecommunication and commercial video broadcasting streams, computer systems logs, and web clicks are categorical or mixed-value data streams that exhibit context-dependency. Models that try to capture this context-dependency tend not to be scalable. This paper offers a solution to the scalability problem of these models by providing a method for generating them around relevant...
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