En raison d'une grève chez bpost, des retards de livraison peuvent survenir. Besoin de quelque chose en urgence ? Optez pour un retrait en magasin ou rendez-vous dans une Librairie Club à proximité.

Retrait en 2 heures
Assortiment impressionnant
Paiement sécurisé
Toujours un magasin près de chez vous

Retrait en 2 heures
Assortiment impressionnant
Paiement sécurisé
Toujours un magasin près de chez vous

Magasins

Wishlist

Magasins

Wishlist

Chercher

Promotions

193,95 €

+ 387 points

Livraison 2 à 3 semaines

Passer une commande en un clic

Payer en toute sécurité

Livraison en Belgique: 3,99 €

Livraison en magasin gratuite

Description

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.