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Similarity-Based Pattern Analysis and Recognition

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Description

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.
Release date Australia
September 17th, 2016
Contributor
  • Edited by Marcello Pelillo
Pages
291
Edition
Softcover reprint of the original 1st ed. 2013
Audience
  • Professional & Vocational
Illustrations
46 Illustrations, color; 19 Illustrations, black and white; XIV, 291 p. 65 illus., 46 illus. in color.
Dimensions
155x235x17
ISBN-13
9781447169505
Product ID
26084360

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