000 | 03267nam a22004935i 4500 | ||
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001 | 978-3-540-71770-6 | ||
003 | DE-He213 | ||
005 | 20161121230718.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2008 gw | s |||| 0|eng d | ||
020 |
_a9783540717706 _9978-3-540-71770-6 |
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024 | 7 |
_a10.1007/978-3-540-71770-6 _2doi |
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050 | 4 | _aQ337.5 | |
050 | 4 | _aTK7882.P3 | |
072 | 7 |
_aUYQP _2bicssc |
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072 | 7 |
_aCOM016000 _2bisacsh |
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082 | 0 | 4 |
_a006.4 _223 |
100 | 1 |
_aFink, Gernot A. _eauthor. |
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245 | 1 | 0 |
_aMarkov Models for Pattern Recognition _h[electronic resource] : _bFrom Theory to Applications / _cby Gernot A. Fink. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008. |
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300 |
_aXII, 248 p. 51 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _aApplication Areas -- Application Areas -- Theory -- Foundations of Mathematical Statistics -- Vector Quantization -- Hidden Markov Models -- n-Gram Models -- Practice -- Computations with Probabilities -- Configuration of Hidden Markov Models -- Robust Parameter Estimation -- Efficient Model Evaluation -- Model Adaptation -- Integrated Search Methods -- Systems -- Speech Recognition -- Character and Handwriting Recognition -- Analysis of Biological Sequences. | |
520 | _aMarkov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications. This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts - covering Hidden Markov models and Markov chain models - and presents the techniques and algorithmic solutions essential to creating real world applications. The actual use of Markov models in their three main application areas - namely speech recognition, handwriting recognition, and biological sequence analysis - is presented with examples of successful systems. Encompassing both Markov model theory and practise, this book addresses the needs of practitioners and researchers from the field of pattern recognition as well as graduate students with a related major field of study. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputational linguistics. | |
650 | 0 | _aImage processing. | |
650 | 0 | _aPattern 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: _z9783540717669 |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-71770-6 |
912 | _aZDB-2-SCS | ||
950 | _aComputer Science (Springer-11645) | ||
999 |
_c502846 _d502846 |