Table of contents
About this book
The pattern recognition and machine learning communities have, until recently, focused mainly on feature-vector representations, typically considering objects in isolation. However, this paradigm is being increasingly challenged by similarity-based approaches, which recognize the importance of relational and similarity information.
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 that provide assistance in the diagnosis of physical and mental illnesses from tissue microarray images and MRI images
This pioneering work is essential reading for graduate students and researchers seeking an introduction to this important and diverse subject.Marcello Pelillo is a Full Professor of Computer Science at the University of Venice, Italy. He is a Fellow of the IEEE and of the IAPR.
Editors and affiliations
- DOI https://doi.org/10.1007/978-1-4471-5628-4
- Copyright Information Springer-Verlag London 2013
- Publisher Name Springer, London
- eBook Packages Computer Science
- Print ISBN 978-1-4471-5627-7
- Online ISBN 978-1-4471-5628-4
- Series Print ISSN 2191-6586
- Series Online ISSN 2191-6594
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