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Algorithmic Advances in Riemannian Geometry and Applications For Machine Learning, Computer Vision, Statistics, and Optimization /
This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Colección: | Advances in Computer Vision and Pattern Recognition,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Algorithmic Advances in Riemannian Geometry and Applications |h [electronic resource] : |b For Machine Learning, Computer Vision, Statistics, and Optimization / |c edited by Hà Quang Minh, Vittorio Murino. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a XIV, 208 p. 55 illus., 51 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Advances in Computer Vision and Pattern Recognition, |x 2191-6594 | |
| 520 | |a This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking. | ||
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Mathematical statistics-Data processing. | |
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 1 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Statistics and Computing. |
| 650 | 2 | 4 | |a Mathematical Applications in Computer Science. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 700 | 1 | |a Minh, Hà Quang. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Murino, Vittorio. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 773 | 0 | |t Springer Nature eBook | |
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| 830 | 0 | |a Advances in Computer Vision and Pattern Recognition, |x 2191-6594 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-45026-1 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||