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001446458 0247_ $$a10.1007/978-981-16-8976-5$$2doi
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001446458 08204 $$a006.3/1$$223/eng/20220517
001446458 1001_ $$aLi, Jinxing.
001446458 24510 $$aInformation fusion:$$bmachine learning methods /$$cJinxing Li, Bob Zhang, David Zhang.
001446458 260__ $$aSingapore :$$bSpringer,$$c2022.
001446458 300__ $$a1 online resource
001446458 504__ $$aIncludes bibliographical references and index.
001446458 5050_ $$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.
001446458 506__ $$aAccess limited to authorized users.
001446458 520__ $$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.
001446458 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 17, 2022).
001446458 650_0 $$aMachine learning.
001446458 650_0 $$aInformation theory.
001446458 655_0 $$aElectronic books.
001446458 7001_ $$aZhang, Bob$$c(Of Aomen da xue)
001446458 7001_ $$aZhang, David,$$d1949-
001446458 77608 $$iPrint version:$$z981168975X$$z9789811689758$$w(OCoLC)1286794106
001446458 852__ $$bebk
001446458 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-8976-5$$zOnline Access$$91397441.1
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001446458 980__ $$aBIB
001446458 980__ $$aEBOOK
001446458 982__ $$aEbook
001446458 983__ $$aOnline
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