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020 _a9781447156284
_9978-1-4471-5628-4
024 7 _a10.1007/978-1-4471-5628-4
_2doi
035 _ato000483528
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.4
_223
100 1 _aPelillo, Marcello.
_eeditor.
_9413503
245 1 0 _aSimilarity-Based Pattern Analysis and Recognition
_helectronic resource
_cedited by Marcello Pelillo.
260 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXIV, 291 p. 65 illus., 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aAdvances in Computer Vision and Pattern Recognition,
_x2191-6586
505 0 _aIntroduction -- 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 _aThe 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 _aComputer Science.
_9155490
650 0 _aOptical pattern recognition.
_9304126
650 1 4 _aComputer Science.
_9155490
650 2 4 _aPattern Recognition.
_9304129
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aAdvances in Computer Vision and Pattern Recognition,
_9413327
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-5628-4
912 _aZDB-2-SCS
999 _c356176