Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by as...
Structured Hidden Markov Models (S-HMM) are a variant of Hierarchical Hidden Markov Models; it provides an abstraction mechanism allowing a high level symbolic description of the k...
Building profiles for processes and for interactive users is a important task in intrusion detection. This paper presents the results obtained with a Hierarchical Hidden Markov Mo...
We study syntax-free models for name-passing processes. For interleaving semantics, we identify the indexing structure required of an early labelled transition system to support t...
This paper introduces a class of statistical mechanisms, called hidden understanding models, for natural language processing. Much of the framework for hidden understanding models...
Scott Miller, Richard M. Schwartz, Robert J. Bobro...