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Similarity-Based Pattern Analysis and Recognition (Record no. 43825)

MARC details
000 -LEADER
fixed length control field 04021nam a22004577a 4500
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
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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 PDF
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>
912 ## -
-- ZDB-2-SCS
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
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No items available.