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Book synopsis

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.§

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Similarity-Based Pattern Analysis and Recognition
• Author: Marcello Pelillo
• Language: English
• Binding: Hardback
• Number of pages: 291
• EAN: 9781447156277
• ISBN: 1447156277
• ID: 02179455
• Publisher: Springer London Ltd
• Weight: 574 g
• Dimensions: 241 × 164 × 17 mm
• Date of publishing: 26. November 2013

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