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Classification Methods for Internet Applications
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SYSNO ASEP 0522793 Document Type B - Monograph R&D Document Type Monograph Title Classification Methods for Internet Applications Author(s) Holeňa, Martin (UIVT-O) SAI, RID
Pulc, P. (CZ)
Kopp, M. (CZ)Issue data Springer: Cham, 2020 ISBN 978-3-030-36961-3 Series Studies in Big Data Series number 69 Number of pages 281 s. Publication form Print - P Language eng - English Country CH - Switzerland Keywords Spam filtering ; Recommender systems ; Malware detection ; Network intrusion detection ; Random forests ; Classifier comprehensibility ; Support vector machines ; Nearest neighbours classification ; Bayesian classifiers Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institutional support UIVT-O - RVO:67985807 DOI 10.1007/978-3-030-36962-0 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021
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