Graph Embedding For Pattern Analysis

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Graph Embedding For Pattern Analysis

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Author : Yun Fu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-19

Graph Embedding For Pattern Analysis written by Yun Fu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-19 with Technology & Engineering categories.

Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

Graph Classification And Clustering Based On Vector Space Embedding

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Author : Kaspar Riesen
language : en
Publisher: World Scientific Publishing Company Incorporated
Release Date : 2010

Graph Classification And Clustering Based On Vector Space Embedding written by Kaspar Riesen and has been published by World Scientific Publishing Company Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector. This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.

Deep Learning On Graphs

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Author : Yao Ma
language : en
Publisher: Cambridge University Press
Release Date : 2021-09-23

Deep Learning On Graphs written by Yao Ma and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Computers categories.

A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Graph Based Representations In Pattern Recognition

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Author : Xiaoyi Jiang
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-10

Graph Based Representations In Pattern Recognition written by Xiaoyi Jiang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-10 with Computers categories.

This book constitutes the refereed proceedings of the 8th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2011, held in Münster, Germany, in May 2011. The 34 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on graph-based representation and characterization, graph matching, classification, and querying, graph-based learning, graph-based segmentation, and applications.

Recognizing Patterns In Signals Speech Images And Videos

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Author : International Association for Pattern Recognition
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-01-04

Recognizing Patterns In Signals Speech Images And Videos written by International Association for Pattern Recognition and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-04 with Computers categories.

This book constitutes the refereed contest reports of the 20th International Conference on Pattern Recognition, ICPR 2010, held in Istanbul, Turkey, in August 2010. The 31 revised full papers presented were carefully reviewed and selected. The papers are organized in topical sections on BiHTR - Bi-modal handwritten Text Recognition, CAMCOM 2010 - Verification of Video Source Camera Competition, CDC - Classifier Domains of Competence, GEPR - Graph Embedding for Pattern Recognition, ImageCLEF@ICPR - Information Fusion Task, ImageCLEF@ICPR - Visual Concept Detection Task, ImageCLEF@ICPR - Robot Vision Task, MOBIO - Mobile Biometry Face and Speaker Verification Evaluation, PR in HIMA - Pattern Recognition in Histopathological Images, SDHA 2010 - Semantic Description of Human Activities.

Graph Based Representations In Pattern Recognition

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Author : Walter Kropatsch
language : en
Publisher: Springer
Release Date : 2013-12-06

Graph Based Representations In Pattern Recognition written by Walter Kropatsch and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-06 with Computers categories.

This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2013, held in Vienna, Austria, in May 2013. The 24 papers presented in this volume were carefully reviewed and selected from 27 submissions. They are organized in topical sections named: finding subregions in graphs; graph matching; classification; graph kernels; properties of graphs; topology; graph representations, segmentation and shape; and search in graphs.

Structural Syntactic And Statistical Pattern Recognition

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Author : Georgy Gimel ́farb
language : en
Publisher: Springer
Release Date : 2012-10-22

Structural Syntactic And Statistical Pattern Recognition written by Georgy Gimel ́farb and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-22 with Computers categories.

This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012 as a satellite event of the 21st International Conference on Pattern Recognition, ICPR 2012. The 80 revised full papers presented together with 1 invited paper and the Pierre Devijver award lecture were carefully reviewed and selected from more than 120 initial submissions. The papers are organized in topical sections on structural, syntactical, and statistical pattern recognition, graph and tree methods, randomized methods and image analysis, kernel methods in structural and syntactical pattern recognition, applications of structural and syntactical pattern recognition, clustering, learning, kernel methods in statistical pattern recognition, kernel methods in statistical pattern recognition, as well as applications of structural, syntactical, and statistical methods.

Graph Based Methods In Computer Vision Developments And Applications

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Author : Bai, Xiao
language : en
Publisher: IGI Global
Release Date : 2012-07-31

Graph Based Methods In Computer Vision Developments And Applications written by Bai, Xiao and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-31 with Computers categories.

Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.

Handbook Of Pattern Recognition And Computer Vision 6th Edition

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Author : Chi Hau Chen
language : en
Publisher: World Scientific
Release Date : 2020-04-04

Handbook Of Pattern Recognition And Computer Vision 6th Edition written by Chi Hau Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-04 with Computers categories.

Written by world-renowned authors, this unique compendium presents the most updated progress in pattern recognition and computer vision (PRCV), fully reflecting the strong international research interests in the artificial intelligence arena.Machine learning has been the key to current developments in PRCV. This useful comprehensive volume complements the previous five editions of the book. It places great emphasis on the use of deep learning in many aspects of PRCV applications, not readily available in other reference text.

Graph Representation Learning

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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.