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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern;

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Structural, Syntactic, and Statistical Pattern Recognition

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition;

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Image and Signal Processing

multispectral colour science, color imaging and applications, digital cultural heritage, document image analysis, graph-based representations, image;

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Bridging The Gap Between Graph Edit Distance And Kernel Machines

In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition;

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Graph Classification And Clustering Based On Vector Space Embedding

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It;

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Similarity-Based Pattern Recognition

This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held;

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Matrix Methods in Data Mining and Pattern Recognition

mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how;

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Perspectives on Pattern Recognition

, based on the extracted features. This book presents topical research in the study of pattern recognition, including pattern recognition;

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Structural, Syntactic, and Statistical Pattern Recognition

from 81 submissions. The accepted papers cover the major topics of current interest in pattern recognition, including classification and;

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Graph-Based Social Media Analysis

, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and;

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Handbook of Graph Grammars and Computing by Graph Transformation

Graph grammars originated in the late 60s, motivated by considerations about pattern recognition and compiler construction. Since then the;

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Recognizing Patterns in Signals, Speech, Images, and Videos

of Competence, GEPR - Graph Embedding for Pattern Recognition, ImageCLEF@ICPR - Information Fusion Task, ImageCLEF@ICPR - Visual Concept Detection Task;

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Robust Representation for Data Analytics

for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition;

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Computational Texture and Patterns

continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation;

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Computational Texture and Patterns

continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation;

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Handbook Of Graph Grammars And Computing By Graph Transformation - Volume 2

Graph grammars originated in the late 60s, motivated by considerations about pattern recognition and compiler construction. Since then, the;

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Graph-theoretic Techniques For Web Content Mining

documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature;

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Handbook Of Pattern Recognition And Computer Vision (3rd Edition)

The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders;

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Genetic Algorithms for Pattern Recognition

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational;

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Genetic Algorithms for Pattern Recognition

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational;

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Computational Intelligence for Pattern Recognition

comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.;

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Graphs for Pattern Recognition

: Preface Pattern recognition, infeasible systems of linear inequalities, and graphs Infeasible monotone systems of constraints Complexes, (hyper;

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Pattern Recognition on Oriented Matroids

Pattern Recognition on Oriented Matroids covers a range of innovative problems in combinatorics, poset and graph theories, optimization;

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Introduction To Pattern Recognition And Machine Learning

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides;

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