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020			_a9783030991425
024	7		_a10.1007/978-3-030-99142-5 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA274.7 _b2022 EB
245	0	0	_aHidden Markov Models and Applications _cedited by Nizar Bouguila, Wentao Fan, Manar Amayri
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 298 páginas) _b157 ilustraciones, 149 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9668313 _aMarkov, Procesos de
650		7	_2embne _9405190 _aProcesos estocásticos
700	1		_aBouguila, Nizar _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFan, Wentao _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aAmayri, Manar _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030991418
776	0	8	_iPrinted edition: _z9783030991432
776	0	8	_iPrinted edition: _z9783030991449
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-99142-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eb _zSI