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Similarity-Based Pattern Analysis and Recognition

Edited by Marcello Pelillo

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normal price: R 4 534.95

Price: R 4 307.95

| book 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.

| product details |



Normally shipped | Available from overseas. Usually dispatched in 14 days
Publisher | Springer London Ltd
Published date | 17 Sep 2016
Language |
Format | Paperback / softback
Pages | 291
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-1-4471-6950-5
Readership Age |
BISAC | computers / computer vision

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