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020			_a9783030991425
024	7		_a10.1007/978-3-030-99142-5 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aQA274.7
072		7	_aTJF _2bicssc
072		7	_aUYS _2bicssc
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090			_aQA274.7EBK
245	1	0	_aHidden Markov Models and Applications _h[electronic resource] / _cedited by Nizar Bouguila, Wentao Fan, Manar Amayri.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUnsupervised and Semi-Supervised Learning, _x2522-8498
505	0		_aChapter1. A Roadmap to Hidden Markov Models and A Review of its Application in Occupancy Estimation -- Chapter2. Bounded asymmetric Gaussian mixture-based hidden Markov models -- Chapter3. Using HMM to model neural dynamics and decode useful signals for neuroprosthetic control -- Chapter4. Fire Detection in Images with Discrete Hidden Markov Models -- Chapter5. Hidden Markov Models: Discrete Feature Selection in Activity Recognition -- Chapter6. Bayesian Inference of Hidden Markov Models using Dirichlet Mixtures -- Chapter7. Online learning of Inverted Beta-Liouville HMMs for Anomaly Detection in Crowd Scenes -- Chapter8. A Novel Continuous Hidden Markov Model for Modeling Positive Sequential Data -- Chapter9. Multivariate Beta-based Hidden Markov Models Applied to Human Activity Recognition -- Chapter10. Multivariate Beta-based Hierarchical Dirichlet Process Hidden Markov Models in Medical Applications -- Chapter11. Shifted-Scaled Dirichlet Based Hierarchical Dirichlet Process Hidden Markov Models with Variational Inference Learning.
520			_aThis book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications. Includes new advances on finite and infinite Hidden Markov Models (HMMs) and their applications from different disciplines; Tackles recent challenges related to the deployment of HMMs in real-life applications (e.g., big data, multimodal data, etc.); Presents new applications of HMMs by considering advancements with respect to inference techniques and recent technological advancements.
650		0	_aSignal processing.
650		0	_aComputer science—Mathematics.
650		0	_aMathematical statistics.
650		0	_aMathematical statistics—Data processing.
650	1	4	_aDigital and Analog Signal Processing.
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aStatistics and Computing.
653		0	_aHidden Markov models
700	1		_aBouguila, Nizar. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFan, Wentao. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aAmayri, Manar. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
830		0	_aUnsupervised and Semi-Supervised Learning, _x2522-8498
856	4	0	_uhttps://doi.org/10.1007/978-3-030-99142-5 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK