Markov models are extremely useful as a general, widely
applicable tool for many areas in statistical pattern
recognition.
This unique text/reference places the formalism of Markov chain
and hidden Markov models at the very center of its examination of
current pattern recognition systems, demonstrating how the models
can be used in a range of different applications. Thoroughly
revised and expanded, this new edition now includes a more detailed
treatment of the EM algorithm, a description of an efficient
approximate Viterbi-training procedure, a theoretical derivation of
the perplexity measure, and coverage of multi-pass decoding based
on "n"-best search. Supporting the discussion of the theoretical
foundations of Markov modeling, special emphasis is also placed on
practical algorithmic solutions.
Topics and features: introduces the formal framework for Markov
models, describing hidden Markov models and Markov chain models,
also known as n-gram models; covers the robust handling of
probability quantities, which are omnipresent when dealing with
these statistical methods; presents methods for the configuration
of hidden Markov models for specific application areas, explaining
the estimation of the model parameters; describes important methods
for efficient processing of Markov models, and the adaptation of
the models to different tasks; examines algorithms for searching
within the complex solution spaces that result from the joint
application of Markov chain and hidden Markov models; reviews key
applications of Markov models in automatic speech recognition,
character and handwriting recognition, and the analysis of
biological sequences.
Researchers, practitioners, and graduate students of pattern
recognition will all find this book to be invaluable in aiding
their understanding of the application of statistical methods in
this area.
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