000			05211cam a2200553Ii 4500
001			ocn927103680
003			OCoLC
005			20190328114813.0
006			m o d
007			cr cnu|||unuuu
008			151028t20162016ne a ob 000 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dN$T _dYDXCP _dCDX _dOCLCF _dUMI _dOPELS _dCOO _dDEBBG _dVT2 _dVGM _dNRC _dU3W _dD6H _dCEF _dOCLCQ _dAU@ _dWYU _dTKN _dOCLCQ
019			_a932322886 _a988674515 _a1008944558 _a1066572101
020			_a9780128027714 _q(electronic bk.)
020			_a0128027711 _q(electronic bk.)
020			_a0128027673
020			_a9780128027677
020			_z9780128027677
035			_a(OCoLC)927103680 _z(OCoLC)932322886 _z(OCoLC)988674515 _z(OCoLC)1008944558 _z(OCoLC)1066572101
050		4	_aQA274.7
072		7	_aMAT _x003000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
082	0	4	_a519.2/33 _223
100	1		_aYu, Shun-Zheng, _eauthor.
245	1	0	_aHidden Semi-Markov models : theory, algorithms and applications / _h[electronic resource] _cShun-Zheng Yu.
264		4	_c�2016
264		1	_aAmsterdam, Netherlands : _bElsevier, _c2016.
300			_a1 online resource : _billustrations.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aComputer science reviews and trends
504			_aIncludes bibliographical references.
588	0		_aOnline resource; title from PDF title page (EBSCO, viewed October 29, 2015).
520			_aHidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.
505	0		_aMachine generated contents note: ch. 1 Introduction -- 1.1. Markov Renewal Process and Semi-Markov Process -- 1.2. Hidden Markov Models -- 1.3. Dynamic Bayesian Networks -- 1.4. Conditional Random Fields -- 1.5. Hidden Semi-Markov Models -- 1.6. History of Hidden Semi-Markov Models -- ch. 2 General Hidden Semi-Markov Model -- 2.1.A General Definition of HSMM -- 2.2. Forward -- Backward Algorithm for HSMM -- 2.3. Matrix Expression of the Forward -- Backward Algorithm -- 2.4. Forward-Only Algorithm for HSMM -- 2.5. Viterbi Algorithm for HSMM -- 2.6. Constrained-Path Algorithm for HSMM -- ch. 3 Parameter Estimation of General HSMM -- 3.1. EM Algorithm and Maximum-Likelihood Estimation -- 3.2. Re-estimation Algorithms of Model Parameters -- 3.3. Order Estimation of HSMM -- 3.4. Online Update of Model Parameters -- ch. 4 Implementation of HSMM Algorithms -- 4.1. Heuristic Scaling -- 4.2. Posterior Notation -- 4.3. Logarithmic Form -- 4.4. Practical Issues in Implementation -- ch. 5 Conventional HSMMs.
505	0		_aNote continued: 5.1. Explicit Duration HSMM -- 5.2. Variable Transition HSMM -- 5.3. Variable-Transition and Explicit-Duration Combined HSMM -- 5.4. Residual Time HSMM -- ch. 6 Various Duration Distributions -- 6.1. Exponential Family Distribution of Duration -- 6.2. Discrete Coxian Distribution of Duration -- 6.3. Duration Distributions for Viterbi HSMM Algorithms -- ch. 7 Various Observation Distributions -- 7.1. Typical Parametric Distributions of Observations -- 7.2.A Mixture of Distributions of Observations -- 7.3. Multispace Probability Distributions -- 7.4. Segmental Model -- 7.5. Event Sequence Model -- ch. 8 Variants of HSMMs -- 8.1. Switching HSMM -- 8.2. Adaptive Factor HSMM -- 8.3. Context-Dependent HSMM -- 8.4. Multichannel HSMM -- 8.5. Signal Model of HSMM -- 8.6. Infinite HSMM and HDP-HSMM -- 8.7. HSMM Versus HMM -- ch. 9 Applications of HSMMs -- 9.1. Speech Synthesis -- 9.2. Human Activity Recognition -- 9.3.Network Traffic Characterization and Anomaly Detection.
650		0	_aMarkov processes.
650		0	_aRenewal theory.
650		7	_aMATHEMATICS _xApplied. _2bisacsh
650		7	_aMATHEMATICS _xProbability & Statistics _xGeneral. _2bisacsh
650		7	_aMarkov processes. _2fast _0(OCoLC)fst01010347
650		7	_aRenewal theory. _2fast _0(OCoLC)fst01094620
655		4	_aElectronic books.
655		0	_aElectronic book.
776	0	8	_iPrint version: _aYu, Shun-Zheng. _tHidden semi-markov models : theory, algorithms and applications. _dAmsterdam, [Netherlands] : Elsevier, �2016 _hix, 195 pages _kComputer science reviews and trends. _z9780128027677
830		0	_aComputer science reviews and trends.
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128027677
999			_c247201 _d247201