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Similarity-Based Pattern Analysis and Recognition
Hardcover
Series: Advances in Computer Vision and Pattern Recognition
ISBN13: 9781447156277
Publisher: Springer Nature
Published: Dec 12 2013
Pages: 291
Weight: 1.34
Height: 0.75 Width: 6.14 Depth: 9.21
Language: English
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.
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