Similarity-Based Pattern Analysis and Recognition (Record no. 43825)
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001 - CONTROL NUMBER | |
control field | sulb-eb0021733 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | BD-SySUS |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20160413122158.0 |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
fixed length control field | cr nn 008mamaa |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 131126s2013 xxk| s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9781447156284 |
-- | 978-1-4471-5628-4 |
024 7# - OTHER STANDARD IDENTIFIER | |
Standard number or code | 10.1007/978-1-4471-5628-4 |
Source of number or code | doi |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | Q337.5 |
Classification number | TK7882.P3 |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UYQP |
Source | bicssc |
Subject category code | COM016000 |
Source | bisacsh |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.4 |
Edition number | 23 |
245 10 - TITLE STATEMENT | |
Title | Similarity-Based Pattern Analysis and Recognition |
Medium | [electronic resource] / |
Statement of responsibility, etc. | edited by Marcello Pelillo. |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Place of production, publication, distribution, manufacture | London : |
Name of producer, publisher, distributor, manufacturer | Springer London : |
-- | Imprint: Springer, |
Date of production, publication, distribution, manufacture, or copyright notice | 2013. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | XIV, 291 p. 65 illus., 46 illus. in color. |
Other physical details | online resource. |
336 ## - CONTENT TYPE | |
Content type term | text |
Content type code | txt |
Source | rdacontent |
337 ## - MEDIA TYPE | |
Media type term | computer |
Media type code | c |
Source | rdamedia |
338 ## - CARRIER TYPE | |
Carrier type term | online resource |
Carrier type code | cr |
Source | rdacarrier |
347 ## - DIGITAL FILE CHARACTERISTICS | |
File type | text file |
Encoding format | |
Source | rda |
490 1# - SERIES STATEMENT | |
Series statement | Advances in Computer Vision and Pattern Recognition, |
International Standard Serial Number | 2191-6586 |
505 0# - FORMATTED CONTENTS NOTE | |
Formatted contents note | Introduction -- Part I: Foundational Issues -- Non-Euclidean Dissimilarities -- SIMBAD -- Part II: Deriving Similarities for Non-vectorial Data -- On the Combination of Information Theoretic Kernels with Generative Embeddings -- Learning Similarities from Examples under the Evidence Accumulation Clustering Paradigm -- Part III: Embedding and Beyond -- Geometricity and Embedding -- Structure Preserving Embedding of Dissimilarity Data -- A Game-Theoretic Approach to Pairwise Clustering and Matching -- Part IV: Applications -- Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma -- Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness. |
520 ## - SUMMARY, ETC. | |
Summary, etc. | 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. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Computer science. |
Topical term or geographic name as entry element | Pattern recognition. |
Topical term or geographic name as entry element | Computer Science. |
Topical term or geographic name as entry element | Pattern Recognition. |
700 1# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Pelillo, Marcello. |
Relator term | editor. |
710 2# - ADDED ENTRY--CORPORATE NAME | |
Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY | |
Title | Springer eBooks |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
Relationship information | Printed edition: |
International Standard Book Number | 9781447156277 |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
Uniform title | Advances in Computer Vision and Pattern Recognition, |
International Standard Serial Number | 2191-6586 |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | <a href="http://dx.doi.org/10.1007/978-1-4471-5628-4">http://dx.doi.org/10.1007/978-1-4471-5628-4</a> |
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942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
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