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024	7		_a10.1007/978-981-16-8976-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aLi, Jinxing _eautor _0(orcid)0000-0001-5156-0305 _1https://orcid.org/0000-0001-5156-0305 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685330
245	1	0	_aInformation Fusion : _bMachine Learning Methods _cby Jinxing Li, Bob Zhang, David Zhang
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XXVI, 260 páginas) _b1 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 1. Introduction -- Chapter 2. Information fusion based on sparse/collaborative representation -- Chapter 3. Information fusion based on gaussian process latent variable model -- Chapter 4. Information fusion based on multi-view and multifeature earning -- Chapter 5. Information fusion based on metric learning -- Chapter 6. Information fusion based on score/weight classifier fusion -- Chapter 7. Information fusion based on deep learning -- Chapter 8. Conclusion.
520			_aIn the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications. This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy, Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, etc. This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, etc. Furthermore, it offers a valuable resource for interdisciplinary research.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9140554 _aInformación, Teoría de la
700	1		_aZhang, Bob _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685331 _c(Of Aomen da xue)
700	1		_aZhang, David, _eautor _0(orcid)0000-0002-5027-5286 _1https://orcid.org/0000-0002-5027-5286 _4aut _4http://id.loc.gov/vocabulary/relators/aut _948572 _d1949-
776	0	8	_iPrinted edition: _z9789811689758
776	0	8	_iPrinted edition: _z9789811689772
776	0	8	_iPrinted edition: _z9789811689789
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-8976-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI