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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.
Produkteigenschaften
- Artikelnummer: 9781447156277
- Medium: Buch
- ISBN: 978-1-4471-5627-7
- Verlag: Springer
- Erscheinungstermin: 12.12.2013
- Sprache(n): Englisch
- Auflage: Erscheinungsjahr 2013
- Serie: Advances in Computer Vision and Pattern Recognition
- Produktform: Gebunden, HC runder Rücken kaschiert
- Gewicht: 6414 g
- Seiten: 291
- Format (B x H x T): 160 x 241 x 23 mm
- Ausgabetyp: Kein, Unbekannt