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Bridging The Gap Between Graph Edit Distance And Kernel Machines
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Book Synopsis Bridging the Gap Between Graph Edit Distance and Kernel Machines by : Michel Neuhaus
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.
Book Synopsis Structural, Syntactic, and Statistical Pattern Recognition by : Georgy Gimel ́farb
Download or read book Structural, Syntactic, and Statistical Pattern Recognition written by Georgy Gimel ́farb and published by Springer. This book was released on 2012-10-22 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Graph-Based Representations in Pattern Recognition by : Andrea Torsello
Download or read book Graph-Based Representations in Pattern Recognition written by Andrea Torsello and published by Springer Science & Business Media. This book was released on 2009-07-09 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2009, held in Venice, Italy in May 2009. The 37 revised full papers presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on graph-based representation and recognition, graph matching, graph clustering and classification, pyramids, combinatorial maps, and homologies, as well as graph-based segmentation.
Book Synopsis Structural Pattern Recognition with Graph Edit Distance by : Kaspar Riesen
Download or read book Structural Pattern Recognition with Graph Edit Distance written by Kaspar Riesen and published by Springer. This book was released on 2016-01-09 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussed in the book.
Book Synopsis Structural, Syntactic, and Statistical Pattern Recognition by : Xiao Bai
Download or read book Structural, Syntactic, and Statistical Pattern Recognition written by Xiao Bai and published by Springer. This book was released on 2018-08-10 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018. The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods.
Book Synopsis Pattern Recognition Applications and Methods by : Maria De Marsico
Download or read book Pattern Recognition Applications and Methods written by Maria De Marsico and published by Springer. This book was released on 2018-06-15 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains revised and extended versions of selected papers from the 6th International Conference on Pattern Recognition, ICPRAM 2017, held in Porto, Portugal, in February 2017. The 13 full papers presented were carefully reviewed and selected from 139 initial submissions. They aim at making visible and understandable the relevant trends of current research on pattern recognition.
Download or read book Pattern Recognition written by Huimin Lu and published by Springer Nature. This book was released on 2023-12-06 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.
Book Synopsis Wavelet Theory Approach to Pattern Recognition by : Yuan Yan Tang
Download or read book Wavelet Theory Approach to Pattern Recognition written by Yuan Yan Tang and published by World Scientific Publishing Company. This book was released on 2009 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ch. 1. Introduction. 1.1. Wavelet : a novel mathematical tool for pattern recognition. 1.2. Brief review of pattern recognition with wavelet theory -- ch. 2. Continuous wavelet transforms. 2.1. General theory of continuous wavelet transforms. 2.2. The continuous wavelet transform as a filter. 2.3. Characterization of Lipschitz regularity of signal by wavelet. 2.4. Some examples of basic wavelets -- ch. 3. Multiresolution analysis and wavelet bases. 3.1. Multiresolution analysis. 3.2. The construction of MRAs. 3.3. The construction of biorthonormal wavelet bases. 3.4. S. mallat algorithms -- ch. 4. Some typical wavelet bases. 4.1. Orthonormal wavelet bases. 4.2. Nonorthonormal wavelet bases -- ch. 5. Step-edge detection by wavelet transform. 5.1. Edge detection with local maximal modulus of wavelet transform. 5.2. Calculation of W[symbol]f(x) and W[symbol]f(x, y). 5.3. Wavelet transform for contour extraction and background removal -- ch. 6. Characterization of dirac-edges with quadratic spline wavelet transform. 6.1. Selection of wavelet functions by derivation. 6.2. Characterization of dirac-structure edges by wavelet transform. 6.3. Experiments -- ch. 7. Construction of new wavelet function and application to curve analysis. 7.1. Construction of new wavelet function - Tang-Yang wavelet. 7.2. Characterization of curves through new wavelet transform. 7.3. Comparison with other wavelets. 7.4. Algorithm and experiments -- ch. 8. Skeletonization of ribbon-like shapes with new wavelet function. 8.1. Tang-Yang wavelet function. 8.2. Characterization of the boundary of a shape by wavelet transform. 8.3. Wavelet skeletons and its implementation. 8.4. Algorithm and experiment -- ch. 9. Feature extraction by wavelet sub-patterns and divider dimensions. 9.1. Dimensionality reduction of two-dimensional patterns with ring-projection. 9.2. Wavelet orthonormal decomposition to produce sub-patterns. 9.3. Wavelet-fractal scheme. 9.4. Experiments -- ch. 10. Document analysis by reference line detection with 2-D wavelet transform. 10.1. Two-dimensional MRA and mallat algorithm. 10.2. Detection of reference line from sub-images by the MRA. 10.3. Experiments -- ch. 11. Chinese character processing with B-spline wavelet transform. 11.1. Compression of Chinese character. 11.2. Enlargement of type size with arbitrary scale based on wavelet transform. 11.3. Generation of Chinese type style based on wavelet transform -- ch. 12. Classifier design based on orthogonal wavelet series. 12.1. Fundamentals. 12.2. Minimum average lose classifier design. 12.3. Minimum error-probability classifier design. 12.4. Probability density estimation based on orthogonal wavelet series
Book Synopsis The British National Bibliography by : Arthur James Wells
Download or read book The British National Bibliography written by Arthur James Wells and published by . This book was released on 2009 with total page 1922 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Foundations of Data Science by : Avrim Blum
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author :Niels da Vitoria Lobo Publisher :Springer Science & Business Media ISBN 13 :3540896880 Total Pages :1029 pages Book Rating :4.5/5 (48 download)
Book Synopsis Structural, Syntactic, and Statistical Pattern Recognition by : Niels da Vitoria Lobo
Download or read book Structural, Syntactic, and Statistical Pattern Recognition written by Niels da Vitoria Lobo and published by Springer Science & Business Media. This book was released on 2008-11-24 with total page 1029 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 and the 7th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008. The 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.
Book Synopsis Managing and Mining Graph Data by : Charu C. Aggarwal
Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Book Synopsis Introduction to Information Retrieval by : Christopher D. Manning
Download or read book Introduction to Information Retrieval written by Christopher D. Manning and published by Cambridge University Press. This book was released on 2008-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
Book Synopsis Structural Pattern Recognition with Graph Edit Distance by : Kaspar Riesen
Download or read book Structural Pattern Recognition with Graph Edit Distance written by Kaspar Riesen and published by Springer. This book was released on 2018-03-30 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussed in the book.
Book Synopsis Computational Topology for Data Analysis by : Tamal Krishna Dey
Download or read book Computational Topology for Data Analysis written by Tamal Krishna Dey and published by Cambridge University Press. This book was released on 2022-03-10 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topological data analysis (TDA) has emerged recently as a viable tool for analyzing complex data, and the area has grown substantially both in its methodologies and applicability. Providing a computational and algorithmic foundation for techniques in TDA, this comprehensive, self-contained text introduces students and researchers in mathematics and computer science to the current state of the field. The book features a description of mathematical objects and constructs behind recent advances, the algorithms involved, computational considerations, as well as examples of topological structures or ideas that can be used in applications. It provides a thorough treatment of persistent homology together with various extensions – like zigzag persistence and multiparameter persistence – and their applications to different types of data, like point clouds, triangulations, or graph data. Other important topics covered include discrete Morse theory, the Mapper structure, optimal generating cycles, as well as recent advances in embedding TDA within machine learning frameworks.
Book Synopsis Automatic Speech and Speaker Recognition by : Joseph Keshet
Download or read book Automatic Speech and Speaker Recognition written by Joseph Keshet and published by John Wiley & Sons. This book was released on 2009-04-27 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
