Similarity-Based Pattern Analysis and Recognition

Nonfiction, Computers, Advanced Computing, Engineering, Optical Data Processing, Computer Vision, General Computing
Cover of the book Similarity-Based Pattern Analysis and Recognition by , Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9781447156284
Publisher: Springer London Publication: November 26, 2013
Imprint: Springer Language: English
Author:
ISBN: 9781447156284
Publisher: Springer London
Publication: November 26, 2013
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer London

Cover of the book Re-engineering of Products and Processes by
Cover of the book Probability Theory by
Cover of the book Risk Navigation Strategies for Major Capital Projects by
Cover of the book Nonlinear Stochastic Systems with Incomplete Information by
Cover of the book An Information Security Handbook by
Cover of the book Transanal Stapling Techniques for Anorectal Prolapse by
Cover of the book An Introduction to Laplace Transforms and Fourier Series by
Cover of the book Pediatric Heart Sounds by
Cover of the book Quality Management in Reverse Logistics by
Cover of the book Video Text Detection by
Cover of the book Mobile Persuasion Design by
Cover of the book Advanced Modeling and Optimization of Manufacturing Processes by
Cover of the book Model-Based Development and Evolution of Information Systems by
Cover of the book Stereotactic Body Radiotherapy by
Cover of the book Platelet-Vessel Wall Interactions by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy