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
|
LSN: |
1-4471-6950-6 |
Barcode: |
9781447169505 |
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