Statistical and Machine Learning Approaches for Network Analysis

Name: Statistical and Machine Learning Approaches for Network Analysis
Brand: John Wiley & Sons Inc
SKU: 9780470195154
Price: 3051 CZK
Availability: InStock

Autor: Matthias Dehmer, Subhash C. Basak

Jazyk:

Vazba: Pevná

Vydavatel: John Wiley & Sons Inc

Dostupnost: Skladem u dodavatele

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Explore the multidisciplinary nature of complex networks through machine learning techniques§Statist...

Informace o knize

Autor

Matthias Dehmer, Subhash C. Basak

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2012

Stránek

344

EAN

9780470195154

ISBN

0470195150

Enbook ID

01388148

Vydavatel

John Wiley & Sons Inc

Hmotnost

610

Rozměry

156 x 241 x 23

Kompletní popis

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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