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Similarity-Based Pattern Analysis and Recognition
- Publication Year :
- 2013
-
Abstract
- 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.
- Subjects :
- Computer vision
Image analysis
Pattern recognition systems
Subjects
Details
- Language :
- English
- ISBNs :
- 9781447156277 and 9781447156284
- Database :
- eBook Index
- Journal :
- Similarity-Based Pattern Analysis and Recognition
- Publication Type :
- eBook
- Accession number :
- 669268