Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16(2003)
摘要
In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an artificially reduced set of states; this is due in large part to the fact that the basic HMM estimation algorithms have a quadratic dependence on the size of the state set. We present algorithms that reduce this computational bottleneck to linear or near-linear time, when the states can be embedded in an underlying grid of parameters. This type of state representation arises in many domains; in particular, we show ail application to traffic analysis at a high-volume Web site.
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关键词
state space,hidden markov model,linear time
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