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dc.contributor.authorIvannikova, Elena
dc.date.accessioned2017-12-08T07:41:46Z
dc.date.available2017-12-08T07:41:46Z
dc.date.issued2017
dc.identifier.isbn978-951-39-7279-0
dc.identifier.otheroai:jykdok.linneanet.fi:1804203
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/56189
dc.description.abstractThe subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking improved production practices and minimized production time and costs. In connection to this, several industrial case studies are presented in which mathematical models for predicting paper quality were proposed. The most important variables for the prediction models are selected based on information-theoretic measures and regression trees approach. The rest of the original papers are devoted to unsupervised machine learning. The main focus is developing advanced spectral clustering techniques for community detection and anomaly detection. As part of these efforts, a number of enhancements for the dependence clustering algorithm have been proposed. These enhancements include adding regularization for controlling the size of clusters, extension to the ensemble version for improving model stability, handling overlapping clusters, and adaptation to solving anomaly detection problems and handling big datasets. Another focus of the thesis is on developing anomaly detection algorithms for network security data. In connection to this, a probabilistic transition-based approach is proposed for detecting application-layer distributed denial-of-service attacks. The developed approaches are tested on real datasets and are capable of efficiently solving the given tasks with high accuracy and good performance. They are shown to be applicable to solving variable selection, graph segmentation, and anomaly detection tasks in different applications.
dc.format.extent1 verkkoaineisto (54 sivua, 38 sivua useina numerointijaksoina, 36 numeroimatonta sivua) : kuvitettu
dc.language.isoeng
dc.publisherUniversity of Jyväskylä
dc.relation.ispartofseriesJyväskylä studies in computing
dc.relation.isversionofJulkaistu myös painettuna.
dc.subject.otherclustering
dc.subject.othercommunity detection
dc.subject.otheranomaly detection
dc.subject.otherpaper machine
dc.subject.otherregression analysis
dc.subject.otherregression trees
dc.subject.othermutual information
dc.subject.othergraph segmentation
dc.subject.otherspectral clustering
dc.subject.othervariable selection
dc.subject.othernetwork security
dc.subject.otherbig data
dc.titleIntelligent solutions for real-life data-driven applications
dc.identifier.urnURN:ISBN:978-951-39-7279-0
dc.type.dcmitypeTexten
dc.type.ontasotVäitöskirjafi
dc.type.ontasotDoctoral dissertationen
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.oppiaineTietotekniikkafi
dc.relation.issn1456-5390
dc.relation.numberinseries270
dc.rights.accesslevelopenAccessfi
dc.subject.ysokoneoppiminen
dc.subject.ysoregressioanalyysi
dc.subject.ysoklusterianalyysi
dc.subject.ysopaperikoneet
dc.subject.ysolaadunvalvonta
dc.subject.ysobig data
dc.subject.ysotiedonlouhinta
dc.subject.ysotietoturva


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