000			03278nam a22004935i 4500
001			978-3-540-71770-6
003			DE-He213
005			20170628034705.0
007			cr nn 008mamaa
008			100301s2008 gw | s |||| 0|eng d
020			_a9783540717706 _9978-3-540-71770-6
024	7		_a10.1007/978-3-540-71770-6 _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.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2008.
300			_aXII, 248 p. 51 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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	_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: _z9783540717669
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-540-71770-6
912			_aZDB-2-SCS
999			_c21057 _d21057