標(biāo)題: Titlebook: Covariances in Computer Vision and Machine Learning; Hà Quang Minh,Vittorio Murino Book 2018 Springer Nature Switzerland AG 2018 [打印本頁] 作者: 毛發(fā) 時(shí)間: 2025-3-21 18:36
書目名稱Covariances in Computer Vision and Machine Learning影響因子(影響力)
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書目名稱Covariances in Computer Vision and Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Covariances in Computer Vision and Machine Learning被引頻次
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書目名稱Covariances in Computer Vision and Machine Learning讀者反饋學(xué)科排名
作者: Infant 時(shí)間: 2025-3-21 22:51 作者: 思想 時(shí)間: 2025-3-22 02:57
Geometry of SPD Matricesd images by covariance matrices, this means that we need to have a similarity measure between covariance matrices. Since covariance matrices, properly regularized if necessary, are symmetric, positive definite (SPD matrices), a natural approach to measuring their similarity is via a distance (or dis作者: SCORE 時(shí)間: 2025-3-22 07:03
Kernel Methods on Covariance Matricesstances and divergences between them, we now discuss some of the most important problems encountered in practical applications, namely classification and regression on SPD matrices. In machine learning, a prominent paradigm for solving classification and regression problems is that of kernel methods作者: Airtight 時(shí)間: 2025-3-22 12:02 作者: 保守 時(shí)間: 2025-3-22 16:21 作者: 保守 時(shí)間: 2025-3-22 20:54
Kernel Methods on Covariance Operatorsan distance, and Log-Hilbert-Schmidt distance and inner product between RKHS covariance operators. In this chapter, we show how the Hilbert-Schmidt and Log-Hilbert-Schmidt distances and inner products can be used to define positive definite kernels, allowing us to apply kernel methods on top of cova作者: 職業(yè) 時(shí)間: 2025-3-22 22:13 作者: 貧困 時(shí)間: 2025-3-23 03:12
Covariances in Computer Vision and Machine Learning978-3-031-01820-6Series ISSN 2153-1056 Series E-ISSN 2153-1064 作者: Calculus 時(shí)間: 2025-3-23 05:37
eir applications in many disciplines in science and engineering. The practical applications of SPD matrices are numerous, including Diffusion Tensor Imaging (DTI) in brain imaging [5, 29, 66, 95], kernel learning [2, 60] in machine learning, radar signal processing [3, 9, 40], and Brain Computer Interface (BCI) applications [7, 8, 24, 100].作者: 歡樂中國 時(shí)間: 2025-3-23 10:22 作者: 逃避責(zé)任 時(shí)間: 2025-3-23 14:08 作者: Aggregate 時(shí)間: 2025-3-23 22:01 作者: Ornament 時(shí)間: 2025-3-24 02:04
Introduction,eir applications in many disciplines in science and engineering. The practical applications of SPD matrices are numerous, including Diffusion Tensor Imaging (DTI) in brain imaging [5, 29, 66, 95], kernel learning [2, 60] in machine learning, radar signal processing [3, 9, 40], and Brain Computer Interface (BCI) applications [7, 8, 24, 100].作者: Presbyopia 時(shí)間: 2025-3-24 03:54 作者: hankering 時(shí)間: 2025-3-24 08:06
Data Representation by Covariance Operatorsis chapter, by employing the feature map viewpoint of kernel methods in machine learning, we generalize covariance matrices to infinite-dimensional covariance operators in RKHS. Since they encode . between input features, they can be employed as a powerful form of data representation, which we explore in subsequent chapters.作者: 吹氣 時(shí)間: 2025-3-24 13:57
Geometry of Covariance Operatorsrators. These distances and divergences can then be directly employed in a practical application, e.g., image classification. We emphasize, however, that the concepts we present below are general and applicable in any application involving the comparison of covariance operators.作者: Alveolar-Bone 時(shí)間: 2025-3-24 18:20 作者: maudtin 時(shí)間: 2025-3-24 22:26
We then present a statistical interpretation of this framework, which shows that assuming that an image can be represented by a covariance matrix is essentially equivalent to assuming that its features are random variables generated by a multivariate Gaussian probability distribution with mean zero作者: 防銹 時(shí)間: 2025-3-25 01:49
d images by covariance matrices, this means that we need to have a similarity measure between covariance matrices. Since covariance matrices, properly regularized if necessary, are symmetric, positive definite (SPD matrices), a natural approach to measuring their similarity is via a distance (or dis作者: Peculate 時(shí)間: 2025-3-25 04:45
stances and divergences between them, we now discuss some of the most important problems encountered in practical applications, namely classification and regression on SPD matrices. In machine learning, a prominent paradigm for solving classification and regression problems is that of kernel methods作者: aesthetician 時(shí)間: 2025-3-25 09:40
is chapter, by employing the feature map viewpoint of kernel methods in machine learning, we generalize covariance matrices to infinite-dimensional covariance operators in RKHS. Since they encode . between input features, they can be employed as a powerful form of data representation, which we explo作者: agenda 時(shí)間: 2025-3-25 13:21 作者: Merited 時(shí)間: 2025-3-25 18:12
an distance, and Log-Hilbert-Schmidt distance and inner product between RKHS covariance operators. In this chapter, we show how the Hilbert-Schmidt and Log-Hilbert-Schmidt distances and inner products can be used to define positive definite kernels, allowing us to apply kernel methods on top of cova作者: Noisome 時(shí)間: 2025-3-25 22:42 作者: exclusice 時(shí)間: 2025-3-26 03:02
978-3-031-00692-0Springer Nature Switzerland AG 2018作者: 幾何學(xué)家 時(shí)間: 2025-3-26 07:26 作者: 不近人情 時(shí)間: 2025-3-26 10:32
an distances and divergences intrinsic to SPD matrices, as described in Chapter 2, it is necessary to define new positive definite kernels based on these distances and divergences. In this chapter, we describe these kernels and the corresponding kernel methods.作者: 調(diào)情 時(shí)間: 2025-3-26 14:02
model . in the input data, can substantially outperform finite-dimensional covariance matrices, which only model . in the input. This performance gain comes at higher computational costs and we showed how to substantially decrease these costs via approximation methods.作者: FAST 時(shí)間: 2025-3-26 17:27
Kernel Methods on Covariance Matricesan distances and divergences intrinsic to SPD matrices, as described in Chapter 2, it is necessary to define new positive definite kernels based on these distances and divergences. In this chapter, we describe these kernels and the corresponding kernel methods.作者: 陶醉 時(shí)間: 2025-3-26 21:14
Conclusion and Future Outlookmodel . in the input data, can substantially outperform finite-dimensional covariance matrices, which only model . in the input. This performance gain comes at higher computational costs and we showed how to substantially decrease these costs via approximation methods.作者: Tidious 時(shí)間: 2025-3-27 01:58
measures between images can then be chosen to be distances/divergences between the corresponding covariance matrices, or equivalently, distances/divergences between the corresponding multivariate Gaussian probability distributions, which will be presented in Chapter 2.作者: Cumbersome 時(shí)間: 2025-3-27 05:57
Data Representation by Covariance Matrices measures between images can then be chosen to be distances/divergences between the corresponding covariance matrices, or equivalently, distances/divergences between the corresponding multivariate Gaussian probability distributions, which will be presented in Chapter 2.作者: Musculoskeletal 時(shí)間: 2025-3-27 10:13
2153-1056 computer vision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications...In this book, we begin by presenting an overview of the {it finite-dimensional covariance matrix} representation approach of images, along 作者: Defense 時(shí)間: 2025-3-27 17:20
Book 2018ision and image processing, they give rise to a powerful data representation, namely the covariance descriptor, with numerous practical applications...In this book, we begin by presenting an overview of the {it finite-dimensional covariance matrix} representation approach of images, along with its s作者: AXIS 時(shí)間: 2025-3-27 19:21
Kernel Methods on Covariance Operatorsrnel machine with the Log-Euclidean distance and inner product presented in Chapter 3 can be viewed as a special case of this framework, with the kernel in the first layer being the linear kernel. Along with kernels defined using the exact Log-Hilbert-Schmidt distance, we present kernels defined usi作者: 文件夾 時(shí)間: 2025-3-27 22:26 作者: LEVY 時(shí)間: 2025-3-28 05:06 作者: 安裝 時(shí)間: 2025-3-28 08:22 作者: Accessible 時(shí)間: 2025-3-28 12:04 作者: freight 時(shí)間: 2025-3-28 15:21
Daniel R. Schwarzgering, und selbst einem literarisch Gebildeten würde es wohl schwer fallen, mehr als ein halbes Dutzend Titel von lyrischen Sammlungen aus den letzten Jahren anzugeben. Die Kenntnis von Gedichten wird durch zuf?llige Begegnungen in Zeitungen, Zeitschriften und Anthologien vermittelt. Die Theater fü作者: Esophagitis 時(shí)間: 2025-3-28 21:53
