Similarity-Based Pattern Analysis And Recognition (Advances In Computer Vision And Pattern Recognition)

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.

• | Author: Marcello Pelillo, Marcello Pelillo
• | Publisher: Springer
• | Publication Date: Sep 17, 2016
• | Number of Pages: 305 pages
• | Language: English
• | Binding: Paperback/Computers
• | ISBN-10: 1447169506
• | ISBN-13: 9781447169505