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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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Graph-Based Representations in Pattern Recognition

, 7 in graph clustering and classification, and 7 in graph-based applications.;

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Graph-theoretic Techniques For Web Content Mining

clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization;

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Similarity-Based Pattern Recognition

; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.;

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Database Systems for Advanced Applications

in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine;

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Database Systems for Advanced Applications

in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine;

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Structural, Syntactic, and Statistical Pattern Recognition

representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis;

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A Graph Theoretic Approach to Heterogeneous Data Clustering

-clustering. First, we propose a semantic-based hierarchical image clustering framework based on multi-user feedback. By treating each user as an;

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Embeddings in Natural Language Processing

development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures;

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Embeddings in Natural Language Processing

development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures;

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Embeddings in Natural Language Processing

development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures;

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Semi-Supervised Learning

al. present an efficient and robust graph-based transductive classification method known as the minimum tree cut (MTC), for large scale applications;

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Differential Topology

comprise vector fields and integral curves, surgery, classification of orientable surfaces, and Whitney's embedding theorem. Suitable for advanced;

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Heterogeneous Graph Representation Learning and Applications

to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Supervised and Unsupervised Learning for Data Science

, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students;

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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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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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Time Series Clustering and Classification

developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification;

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Time Series Clustering and Classification

relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based;

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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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Advances in Knowledge Discovery and Data Mining

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on;

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Advances in Knowledge Discovery and Data Mining

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on;

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Advances in Knowledge Discovery and Data Mining

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on;

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Graph Data Mining

challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced;

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Introduction to Computational Proteomics

. It also describes algorithms for assessing similarity between proteins based on sequence and structure analysis as well as mathematical;

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