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Graph-Powered Machine Learning

naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs;

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Statistical and Machine Learning Approaches for Network Analysis

weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross;

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Graph Kernels

Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow the modelling;

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Semi-Supervised Learning

Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very;

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Graph Algorithms Practical Examples in Apache Spark and Neo4j

learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used;

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Learn PySpark

processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark;

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Learn PySpark

data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark;

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Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble;

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Graph-Based Representations in Pattern Recognition

analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph;

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Cooperative and Graph Signal Processing

latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control;

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning;

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Graph-Based Social Media Analysis

, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal;

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Web Information Systems Engineering - WISE 2019

linkage and disambiguation; graph learning; knowledge graphs; graph mining; and text mining.;

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Spark in Action

data sets. But Spark goes much further than other frameworks. By including machine learning and graph processing capabilities, it makes;

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Graph-theoretic Techniques For Web Content Mining

This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms;

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Artificial Intelligence for Data-Driven Medical Diagnosis

Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and;

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Introduction to Data Science

field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and;

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Spark GraphX in Action

implementations. Along the way, it details practical techniques for enhancing applications and applying machine learning algorithms to graph data;

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Applied Recommender Systems with Python

K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each;

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Applied Recommender Systems with Python

K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each;

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Web and Big Data

; Data Management; Topic Model and Language Model Learning; Text Analysis; Text Classification; Machine Learning; Knowledge Graph; Emerging Data;

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Knowledge Seeker - Ontology Modelling for Information Search and Management

components. First, it defines the knowledge representation model !V Ontology Graph. Second, an ontology learning process that based on chi-square;

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Machine Learning and Big Data

viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture;

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Quantitative Graph Theory

Network complexity measures and other topological indices Quantitative approaches to graphs using machine learning (e.g., clustering;

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Quantitative Graph Theory

Network complexity measures and other topological indices Quantitative approaches to graphs using machine learning (e.g., clustering;

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Heterogeneous Graph Representation Learning and Applications

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to;

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Mastering Java Machine Learning

in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining. * Apply;

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