Classification Methods for Internet Applications

0522793 - ÚI 2021 RIV CH eng B - Monography
Holeňa, Martin - Pulc, P. - Kopp, M.
Classification Methods for Internet Applications.
Springer: Cham, 2020. 281 s. Studies in Big Data, 69. ISBN 978-3-030-36961-3
Institutional support: RVO:67985807
Keywords : Spam filtering * Recommender systems * Malware detection * Network intrusion detection * Random forests * Classifier comprehensibility * Support vector machines * Nearest neighbours classification * Bayesian classifiers
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various statistical and machine learning methods, including K nearest neighbours, Bayesian classifiers, the logit method, discriminant analysis, several kinds of artificial neural networks, support vector machines, classification trees and other kinds of rule-based methods, as well as random forests and other kinds of classifier ensembles. The book covers a wide range of available classification methods and their variants, not only those that have already been used in the considered kinds of applications, but also those that have the potential to be used in them in the future. The book is a valuable resource for post-graduate students and professionals alike.
Permanent Link: http://hdl.handle.net/11104/0307224