Similarity-Based Pattern Analysis and Recognition (Paperback, Softcover reprint of the original 1st ed. 2013)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Advances in Computer Vision and Pattern Recognition
Release date:	September 2016
First published:	2013
Editors:	Marcello Pelillo
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Paperback
Pages:	291
Edition:	Softcover reprint of the original 1st ed. 2013
ISBN-13:	978-1-4471-6950-5
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Promotions
LSN:	1-4471-6950-6
Barcode:	9781447169505

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