Mustererkennung bildet die Grundlage fur die Losung verschiedenster Problembereiche in der Informatik. Spracherkennung, Schrifterkennung, Analyse biologischer Sequenzen: mit diesem Lehrbuch gelingt Ihnen der fundierte Einstieg in Theorie und Praxis. "
Mustererkennung bildet die Grundlage fur die Losung verschiedenster Problembereiche in der Informatik. Spracherkennung, Schrifterkennung, Analyse ...
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both...
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research prob...
This thoroughly revised and expanded 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. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of...
This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate...
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...
Markov models are extremely useful as a general, widely applicable tool for many areas in statistical pattern recognition.
