| 书目名称 | Similarity-Based Pattern Analysis and Recognition |
| 编辑 | Marcello Pelillo |
| 视频video | |
| 概述 | Provides a coherent overview of the emerging field of non-Euclidean similarity learning.Presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely |
| 丛书名称 | Advances in Computer Vision and Pattern Recognition |
| 图书封面 |  |
| 描述 | .This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms; reviews similarity measures for non-vectorial data, considering both a “kernel tailoring” approach and a strategy for learning similarities directly from training data; describes various methods for “structure-preserving” embeddings of structured data; formulates classical pattern recognition problems from a purely game-theoretic perspective; examines two large-scale biomedical imagingapplications.. |
| 出版日期 | Book 2013 |
| 关键词 | Computer Vision; Image Analysis; Machine Learning; Pattern Recognition |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4471-5628-4 |
| isbn_softcover | 978-1-4471-6950-5 |
| isbn_ebook | 978-1-4471-5628-4Series ISSN 2191-6586 Series E-ISSN 2191-6594 |
| issn_series | 2191-6586 |
| copyright | Springer-Verlag London 2013 |
发表于 2025-3-21 17:44:12
