Download or read book Graph Kernels written by Karsten Borgwardt and published by . This book was released on 2020-12-22 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kernels for Structured Data written by Thomas Gartner and published by World Scientific. This book was released on 2008 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.
Download or read book Bridging the Gap Between Graph Edit Distance and Kernel Machines written by Michel Neuhaus and published by World Scientific. This book was released on 2007 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain ? commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.
Download or read book Graph Based Representations in Pattern Recognition written by Cheng-Lin Liu and published by Springer. This book was released on 2015-05-04 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015, held in Beijing, China, in May 2015. The 36 papers presented in this volume were carefully reviewed and selected from 53 submissions. The accepted papers cover diverse issues of graph-based methods and applications, with 7 in graph representation, 15 in graph matching, 7 in graph clustering and classification, and 7 in graph-based applications.
Download or read book Learning Theory and Kernel Machines written by Bernhard Schölkopf and published by Springer. This book was released on 2003-11-11 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Download or read book Bridging the Gap Between Graph Edit Distance and Kernel Machines written by Michel Neuhaus and published by World Scientific. This book was released on 2007 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain OCo commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time."
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Learning Theory and Kernel Machines written by Bernhard Schoelkopf and published by Springer Science & Business Media. This book was released on 2003-08-11 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Download or read book Graph Based Representations in Pattern Recognition written by Xiaoyi Jiang and published by Springer Science & Business Media. This book was released on 2011-05-10 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Quantitative Graph Theory written by Matthias Dehmer and published by CRC Press. This book was released on 2014-10-27 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book devoted exclusively to quantitative graph theory, Quantitative Graph Theory: Mathematical Foundations and Applications presents and demonstrates existing and novel methods for analyzing graphs quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical technique
Download or read book Graph Based Representations in Pattern Recognition written by Pasquale Foggia and published by Springer. This book was released on 2017-05-08 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2017, held in Anacapri, Italy, in May 2017. The 25 full papers and 2 abstracts of invited papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers discuss research results and applications in the intersection of pattern recognition, image analysis, graph theory, and also the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics.
Download or read book Trends in Biomathematics Chaos and Control in Epidemics Ecosystems and Cells written by Rubem P. Mondaini and published by Springer Nature. This book was released on 2021-07-27 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers together selected, peer-reviewed papers presented at the BIOMAT 2020 International Symposium, which was virtually held on November 1-6, 2020, with an organization staff based in Rio de Janeiro, Brazil. Topics covered in this volume include infection modeling, with an emphasis on different aspects of the COVID-19 and novel Coronavirus spread; a description of the effectiveness of quarantine measures via dynamic analysis of SLIR model; hemodynamic simulations in time-dependent domains; an optimal control model for the Ebola disease; and the co-existence of chaos and control in the context of biological models. Texts in agroforestry, economic development, and wastewater treatment processes complete this volume. Held every year since 2001, the BIOMAT International Symposium gathers together, in a single conference, researchers from Mathematics, Physics, Biology, and affine fields to promote the interdisciplinary exchange of results, ideas and techniques, promoting truly international cooperation for problem discussion. The 20th edition of the BIOMAT International Symposium has received contributions by authors from 18 countries: Algeria, Brazil, Cameroon, Canada, Chile, China (Hong Kong), Colombia, Germany, Hungary, India, Italy, Morocco, Nigeria, Russia, Senegal, South Africa, USA, and Uzbekistan. Previous BIOMAT volumes with selected works from 2017, 2018, and 2019 were also published by Springer.
Download or read book Image Processing and Analysis with Graphs written by Olivier Lezoray and published by CRC Press. This book was released on 2017-07-12 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering the theoretical aspects of image processing and analysis through the use of graphs in the representation and analysis of objects, Image Processing and Analysis with Graphs: Theory and Practice also demonstrates how these concepts are indispensible for the design of cutting-edge solutions for real-world applications. Explores new applications in computational photography, image and video processing, computer graphics, recognition, medical and biomedical imaging With the explosive growth in image production, in everything from digital photographs to medical scans, there has been a drastic increase in the number of applications based on digital images. This book explores how graphs—which are suitable to represent any discrete data by modeling neighborhood relationships—have emerged as the perfect unified tool to represent, process, and analyze images. It also explains why graphs are ideal for defining graph-theoretical algorithms that enable the processing of functions, making it possible to draw on the rich literature of combinatorial optimization to produce highly efficient solutions. Some key subjects covered in the book include: Definition of graph-theoretical algorithms that enable denoising and image enhancement Energy minimization and modeling of pixel-labeling problems with graph cuts and Markov Random Fields Image processing with graphs: targeted segmentation, partial differential equations, mathematical morphology, and wavelets Analysis of the similarity between objects with graph matching Adaptation and use of graph-theoretical algorithms for specific imaging applications in computational photography, computer vision, and medical and biomedical imaging Use of graphs has become very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Accounting for the wide variety of problems being solved with graphs in image processing and computer vision, this book is a contributed volume of chapters written by renowned experts who address specific techniques or applications. This state-of-the-art overview provides application examples that illustrate practical application of theoretical algorithms. Useful as a support for graduate courses in image processing and computer vision, it is also perfect as a reference for practicing engineers working on development and implementation of image processing and analysis algorithms.
Download or read book Biocomputing 2007 Proceedings Of The Pacific Symposium written by Russ B Altman and published by World Scientific. This book was released on 2006-12-11 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Pacific Symposium on Biocomputing (PSB) 2007 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2007 will be held January 3-7, 2007 at the Grand Wailea, Maui. Tutorials will be offered prior to the start of the conference.PSB 2007 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's “hot topics.” In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
Download or read book Kernels for Structured Data written by Thomas Grtner and published by World Scientific. This book was released on 2008 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.
Download or read book The Semantic Web ESWC 2020 Satellite Events written by Andreas Harth and published by Springer Nature. This book was released on 2020-11-10 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter “ABECTO: An ABox Evaluation and Comparison Tool for Ontologies” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Download or read book Machine Learning and Interpretation in Neuroimaging written by Irina Rish and published by Springer. This book was released on 2016-09-12 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers from the 4th International Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2014, held in Montreal, QC, Canada, in December 2014 as a satellite event of the 11th annual conference on Neural Information Processing Systems, NIPS 2014. The 10 MLINI 2014 papers presented in this volume were carefully reviewed and selected from 17 submissions. They were organized in topical sections named: networks and decoding; speech; clinics and cognition; and causality and time-series. In addition, the book contains the 3 best papers presented at MLINI 2013.
