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Titlebook: Discriminative Learning in Biometrics; David Zhang,Yong Xu,Wangmeng Zuo Book 2016 Springer Science+Business Media Singapore 2016 Biometric

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樓主: Iodine
31#
發(fā)表于 2025-3-27 00:00:40 | 只看該作者
Metric Learning with Biometric Applicationsesent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the triplet-SVM, are also introduced in this chapter.
32#
發(fā)表于 2025-3-27 03:42:42 | 只看該作者
33#
發(fā)表于 2025-3-27 06:49:47 | 只看該作者
https://doi.org/10.1007/978-981-10-2056-8Biometrics; Discriminative learning; Palmprint authentication; Face recognition; Multi-biometrics; Patter
34#
發(fā)表于 2025-3-27 11:54:31 | 只看該作者
978-981-10-9515-3Springer Science+Business Media Singapore 2016
35#
發(fā)表于 2025-3-27 14:34:53 | 只看該作者
https://doi.org/10.1007/978-981-97-3629-4irst give an overview on the systems in terms of the input features and common applications. After that, we will provide a self-contained introduction to some discriminative learning tools that are commonly used in biometrics. A clear understanding of these techniques could be of essential importanc
36#
發(fā)表于 2025-3-27 18:48:39 | 只看該作者
https://doi.org/10.1007/978-981-19-4859-6esent two novel metric learning methods based on a support vector machine (SVM). We then present a kernel classification framework for metric learning that can be implemented efficiently by using the standard SVM solvers. Some novel kernel metric learning methods, such as the double-SVM and the trip
37#
發(fā)表于 2025-3-27 23:40:21 | 只看該作者
38#
發(fā)表于 2025-3-28 03:20:15 | 只看該作者
39#
發(fā)表于 2025-3-28 06:28:32 | 只看該作者
40#
發(fā)表于 2025-3-28 11:21:49 | 只看該作者
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