Matthias Dehmer's Books

Computational Network Theory

Theoretical Foundations and Applications

By: Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl

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Computational Network Theory

Theoretical Foundations and Applications

By: Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

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Page Count

200

Publisher

John Wiley & Sons

Published Date

ISBN 10

3527691537

ISBN 13

9783527691531

Statistical and Machine Learning Approaches for Network Analysis

By: Matthias Dehmer,Subhash C. Basak

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Statistical and Machine Learning Approaches for Network Analysis

By: Matthias Dehmer,Subhash C. Basak

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

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Page Count

269

Publisher

John Wiley & Sons

Published Date

ISBN 10

111834698X

ISBN 13

9781118346983

Mathematical Foundations of Data Science Using R

By: Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

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Mathematical Foundations of Data Science Using R

By: Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.

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Page Count

444

Publisher

Walter de Gruyter GmbH & Co KG

Published Date

ISBN 10

3110796171

ISBN 13

9783110796179

Commutation Relations, Normal Ordering, and Stirling Numbers

By: Toufik Mansour,Matthias Schork

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Commutation Relations, Normal Ordering, and Stirling Numbers

By: Toufik Mansour,Matthias Schork

Commutation Relations, Normal Ordering, and Stirling Numbers provides an introduction to the combinatorial aspects of normal ordering in the Weyl algebra and some of its close relatives. The Weyl algebra is the algebra generated by two letters U and V subject to the commutation relation UV - VU = I. It is a classical result that normal ordering pow

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Page Count

506

Publisher

CRC Press

Published Date

ISBN 10

1466579897

ISBN 13

9781466579897

Computational Network Theory

Theoretical Foundations and Applications

By: Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl

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Computational Network Theory

Theoretical Foundations and Applications

By: Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl

Diese umfassende Einführung in die rechnergestützte Netzwerktheorie als ein Zweig der Netzwerktheorie baut auf dem Grundsatz auf, dass solche Netzwerke als Werkzeuge zu verstehen sind, mit denen sich durch die Anwendung rechnergestützter Verfahren auf große Mengen an Netzwerkdaten Hypothesen ableiten und verifizieren lassen. Ein Team aus erfahrenden Herausgebern und renommierten Autoren aus der ganzen Welt präsentieren und erläutern eine Vielzahl von repräsentativen Methoden der rechnergestützten Netzwerktheorie, die sich aus der Graphentheorie, rechnergestützten und statistischen Verfahren ableiten. Dieses Referenzwerk überzeugt durch einen einheitlichen Aufbau und Stil und eignet sich auch für Kurse zu rechnergestützten Netzwerken.

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Page Count

278

Publisher

John Wiley & Sons

Published Date

ISBN 10

3527691545

ISBN 13

9783527691548

Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

By: Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

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Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

By: Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.

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Page Count

582

Publisher

Springer Nature

Published Date

ISBN 10

3031133390

ISBN 13

9783031133398

Statistical and Machine Learning Approaches for Network Analysis

By: Matthias Dehmer,Subhash C. Basak

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Statistical and Machine Learning Approaches for Network Analysis

By: Matthias Dehmer,Subhash C. Basak

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Page Count

269

Publisher

John Wiley & Sons

Published Date

ISBN 10

111834698X

ISBN 13

9781118346983

Mathematical Foundations of Data Science Using R

By: Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

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Mathematical Foundations of Data Science Using R

By: Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer

In order best exploit the incredible quantities of data being generated in most diverse disciplines data sciences increasingly gain worldwide importance. The book gives the mathematical foundations to handle data properly. It introduces basics and functionalities of the R programming language which has become the indispensable tool for data sciences. Thus it delivers the reader the skills needed to build own tool kits of a modern data scientist.

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Page Count

485

Publisher

Walter de Gruyter GmbH & Co KG

Published Date

ISBN 10

3110565021

ISBN 13

9783110565027

Frontiers in Data Science

By: Matthias Dehmer,Frank Emmert-Streib

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Frontiers in Data Science

By: Matthias Dehmer,Frank Emmert-Streib

Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. "--Provided by publisher.

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Page Count

392

Published Date

ISBN 10

1315156407

ISBN 13

9781315156408

Book's by Matthias Dehmer

Computational Network Theory

Computational Network Theory

Statistical and Machine Learning Approaches for Network Analysis

Statistical and Machine Learning Approaches for Network Analysis

Mathematical Foundations of Data Science Using R

Mathematical Foundations of Data Science Using R

Commutation Relations, Normal Ordering, and Stirling Numbers

Commutation Relations, Normal Ordering, and Stirling Numbers

Computational Network Theory

Computational Network Theory

Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Statistical and Machine Learning Approaches for Network Analysis

Statistical and Machine Learning Approaches for Network Analysis

Mathematical Foundations of Data Science Using R

Mathematical Foundations of Data Science Using R

Frontiers in Data Science

Frontiers in Data Science

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