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_a10.1007/978-981-16-8976-5 _2doi |
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_aLi, Jinxing. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aInformation Fusion _h[electronic resource] : _bMachine Learning Methods / _cby Jinxing Li, Bob Zhang, David Zhang. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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300 |
_aXXVI, 260 p. 1 illus. _bonline resource. |
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_acomputer _bc _2rdamedia |
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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. | ||
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aData mining. | |
650 | 1 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
700 | 1 |
_aZhang, Bob. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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700 | 1 |
_aZhang, David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9789811689758 |
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_iPrinted edition: _z9789811689772 |
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_iPrinted edition: _z9789811689789 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-8976-5 |
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