Similarity-Based Pattern Analysis and Recognition - Advances in Computer Vision and Pattern Recognition (Paperback)Marcello Pelillo (editor)
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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 imaging applications.
Publisher: Springer London Ltd
Number of pages: 291
Weight: 4686 g
Dimensions: 235 x 155 x 17 mm
Edition: Softcover reprint of the original 1st ed. 201
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