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020 _a9781447163084
_9978-1-4471-6308-4
024 7 _a10.1007/978-1-4471-6308-4
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.4
_223
100 1 _aFink, Gernot A.
_eauthor.
245 1 0 _aMarkov Models for Pattern Recognition
_h[electronic resource] :
_bFrom Theory to Applications /
_cby Gernot A. Fink.
250 _a2nd ed. 2014.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2014.
300 _aXIII, 276 p. 45 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _aIntroduction -- Application Areas -- Part I: Theory -- Foundations of Mathematical Statistics -- Vector Quantization and Mixture Estimation -- Hidden Markov Models -- N-Gram Models -- Part II: Practice -- Computations with Probabilities -- Configuration of Hidden Markov Models -- Robust Parameter Estimation -- Efficient Model Evaluation -- Model Adaptation -- Integrated Search Methods -- Part III: Systems -- Speech Recognition -- Handwriting Recognition -- Analysis of Biological Sequences.
520 _aMarkov 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.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aTranslators (Computer programs).
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aPattern Recognition.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aLanguage Translation and Linguistics.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447163077
830 0 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-6308-4
912 _aZDB-2-SCS
999 _c16415
_d16415