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Beschreibung
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.
Details
| Verlag | Springer London |
| Ersterscheinung | 17. September 2016 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 470 Gramm |
| Format | Softcover |
| ISBN-13 | 9781447169505 |
| Auflage | Softcover reprint of the original 1st ed. 2013 |
| Seiten | 291 |
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