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Information fusionmachine learning methods /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Information fusionby Jinxing Li, Bob Zhang, David Zhang.
其他題名:	machine learning methods /
作者:	Li, Jinxing.
其他作者:	Zhang, Bob.
出版者:	Singapore :Springer Nature Singapore :2022.
面頁冊數:	xxvi, 260 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-981-16-8976-5
ISBN:	9789811689765$q(electronic bk.)

Information fusionmachine learning methods /
Li, Jinxing.

Information fusionmachine learning methods /[electronic resource] :by Jinxing Li, Bob Zhang, David Zhang. - Singapore :Springer Nature Singapore :2022. - xxvi, 260 p. :ill., digital ;24 cm.

Chapter 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.

In 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.

ISBN: 9789811689765$q(electronic bk.)

Standard No.: 10.1007/978-981-16-8976-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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505 0 $a Chapter 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 $a In 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.
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