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Similarity-Based Pattern Analysis and Recognition

ACVPR: SIMILARITY-BASED PATTERN ANALYSIS & RECOGNITION

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

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.

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title.
Title Page.
Copyright Page.
Dedication.
Other Frontmatter.
Foreword.
Preface.
Contents.
1: Introduction: The SIMBAD Project.
2: Foundational Issues.
3: Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness.
4: SIMBAD: Emergence of Pattern Similarity.
5: Deriving Similarities for Non-Vectorial Data.
6: On the Combination of Information-Theoretic Kernels with Generative Embeddings.
7: Learning Similarities from Examples under the Evidence Accumulation Clustering Paradigm.
8: Embedding and Beyond.
9: Geometricity and Embedding.
10: Structure Preserving Embedding of Dissimilarity Data.
11: A Game-Theoretic Approach to Pairwise Clustering and Matching.
12: Applications.
13: Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma.
14: Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness.
Index.

Similarity-Based Pattern Analysis and Recognition.css-1p8rvzy{position:absolute;padding-left:8px;}

Similarity-Based Pattern Analysis and Recognition