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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

2013 | OriginalPaper | Buchkapitel

Chapter 1. Introduction: The SIMBAD Project

Marcello Pelillo

Foundational Issues

Frontmatter

2013 | OriginalPaper | Buchkapitel

Chapter 2. Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness

Robert P. W. Duin, Elżbieta Pękalska, Marco Loog

2013 | OriginalPaper | Buchkapitel

Chapter 3. SIMBAD: Emergence of Pattern Similarity

Joachim M. Buhmann

Deriving Similarities for Non-vectorial Data

Frontmatter

2013 | OriginalPaper | Buchkapitel

Chapter 4. On the Combination of Information-Theoretic Kernels with Generative Embeddings

Pedro M. Q. Aguiar, Manuele Bicego, Umberto Castellani, Mário A. T. Figueiredo, André T. Martins, Vittorio Murino, Alessandro Perina, Aydın Ulaş

2013 | OriginalPaper | Buchkapitel

Chapter 5. Learning Similarities from Examples Under the Evidence Accumulation Clustering Paradigm

Ana L. N. Fred, André Lourenço, Helena Aidos, Samuel Rota Bulò, Nicola Rebagliati, Mário A. T. Figueiredo, Marcello Pelillo

Embedding and Beyond

Frontmatter

2013 | OriginalPaper | Buchkapitel

Chapter 6. Geometricity and Embedding

Peng Ren, Furqan Aziz, Lin Han, Eliza Xu, Richard C. Wilson, Edwin R. Hancock

2013 | OriginalPaper | Buchkapitel

Chapter 7. Structure Preserving Embedding of Dissimilarity Data

Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, Joachim M. Buhmann

2013 | OriginalPaper | Buchkapitel

Chapter 8. A Game-Theoretic Approach to Pairwise Clustering and Matching

Marcello Pelillo, Samuel Rota Bulò, Andrea Torsello, Andrea Albarelli, Emanuele Rodolà

Applications

Frontmatter

2013 | OriginalPaper | Buchkapitel

Chapter 9. Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma

Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann

2013 | OriginalPaper | Buchkapitel

Chapter 10. Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness

Aydın Ulaş, Umberto Castellani, Manuele Bicego, Vittorio Murino, Marcella Bellani, Michele Tansella, Paolo Brambilla

Backmatter

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