Statistical and Machine Learning Approaches for Network Analysis

✍ Scribed by Matthias Dehmer, Subhash C. Basak

Publisher: Wiley
Year: 2012
Tongue: English
Leaves: 332
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

📜 SIMILAR VOLUMES

Statistical and Machine Learning Approac

📁 Statistical and Machine Learning Approaches for Network Analysis

✍ Matthias Dehmer (ed.), Subhash C. Basak (ed.) 📂 Library 📅 2012 🏛 Wiley 🌐 English

Explore the multidisciplinary nature of complex networks through machine learning techniquesStatistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on g

Statistics for data science: leverage th

📁 Statistics for data science: leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks

✍ Miller, James D 📂 Library 📅 2017;2018 🏛 Packt 🌐 English

« Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes

Statistics for data science: leverage th

📁 Statistics for data science: leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks

✍ Miller, James D 📂 Library 📅 2017 🏛 Packt 🌐 English

Statistical Machine Learning for Human B

📁 Statistical Machine Learning for Human Behaviour Analysis

✍ Thomas Moeslund, Sergio Escalera 📂 Library 📅 2020 🏛 MDPI AG 🌐 English

This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal

Dynamics On and Of Complex Networks III:

📁 Dynamics On and Of Complex Networks III: Machine Learning and Statistical Physics Approaches

✍ Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, B 📂 Library 📅 2019 🏛 Springer International Publishing 🌐 English

This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predi

Interpretability for Industry 4.0 : Stat

📁 Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

✍ Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi 📂 Library 📅 2022 🏛 Springer 🌐 English

This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of In