SVMhmm: Learns a hidden Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag sequences for new sentences.
In this paper, we consider the problem of filtering in relational
hidden Markov models. We present a compact representation for
such models and an associated logical particle filtering algorithm. Each
particle contains a logical formula that describes a set of states. The
algorithm updates the formulae as new observations are received. Since
a single particle tracks many states, this filter can be more accurate
than a traditional particle filter in high dimensional state spaces, as we
demonstrate in experiments.
