000			03991nam a2200433 c 4500
988			_aSpringer_Engineering_2020
999			_c114398 _d114398 _x1
001			114398
003			ES-MaUEC
005			20230110040220.0
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007			cr nn nnnaamaa
008			190813s2020 gw a o |||| 0|eng d
020			_a9783030238766
024	7		_a10.1007/978-3-030-23876-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA274.4 _b2020 EB
245	0	0	_aMixture models and applications _cedited by Nizar Bouguila, Wentao Fan
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XII, 355 páginas) _b120 ilustraciones, 88 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-848X
490	0		_aEngineering (Springer-11647)
505	0		_aA Gaussian Mixture Model Approach To Classifying Response Types -- Interactive Generation Of Calligraphic Trajectories From Gaussian Mixtures -- Mixture models for the analysis, edition, and synthesis of continuous time series -- Multivariate Bounded Asymmetric Gaussian Mixture Model -- Online Recognition Via A Finite Mixture Of Multivariate Generalized Gaussian Distributions -- L2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of Von Mises Distributions with Localized Feature Selection -- Deriving Probabilistic SVM Kernels From Exponential Family Approximations to Multivariate Distributions for Count Data -- Toward an Efficient Computation of Log-likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering -- A Frequentist Inference Method Based On Finite Bivariate And Multivariate Beta Mixture Models -- Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting -- Online Variational Learning for Medical Image Data Clustering -- Color Image Segmentation using Semi-Bounded Finite Mixture Models by Incorporating Mean Templates -- Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models -- Flexible Statistical Learning Model For Unsupervised Image Modeling And Segmentation.
520	3		_aThis book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
650		7	_2embne _aProcesos estocásticos _9405190
650		7	_2embne _aProbabilidades _9405075
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
776	0	8	_iPrinted edition: _z9783030238759
776	0	8	_iPrinted edition: _z9783030238773
776	0	8	_iPrinted edition: _z9783030238780
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-23876-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI