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Topics in Computational Hidden State Modeling (Thesis)

Report ID:
April 1997
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Motivated by the goal of establishing stochastic and information
theoretic foundations for the study of intelligence and synthesis of
intelligent machines, this thesis probes several topics relating to
hidden state stochastic models.

Finite Growth Models (FGM) are introduced. These are nonnegative
functionals that arise from parametrically-weighted directed acyclic
graphs and a tuple observation that affects these weights. Using FGMs
the parameters of a highly general form of stochastic transducer can
be learned from examples, and the particular case of stochastic string
edit distance is developed. Experiments are described that illustrate
the application of learned string edit distance to the problem of
recognizing a spoken word given a phonetic transcription of the
acoustic signal. With FGMs one may direct learning by criteria
beyond simple maximum-likelihood. The MAP (maximum a posteriori
estimate) and MDL (minimum description length) are discussed along
with the application to causal-context probability models and
unnormalized noncausal models. The FGM framework, algorithms, and
data structures describe hidden Markov models, stochastic context free
grammars, and many other conventional similar models while providing a
unified and natural way for computer scientists to learn and reason
about them and their many variations. A software system and scripting
language is proposed to serve as an assembly language or sorts for
many higher level model types.

This thesis also illuminates certain fundamental aspects of the nature
of normal (Gaussian) mixtures and the reparameterization of related
optimization problems. The use of conditional normal mixtures is
proposed as a tool for image modeling, and issues relating to the
estimation of their parameters are discussed.

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