Semi-Supervised Learning for Hierarchical Networks

DC Field Value Language
dc.contributor.advisor신현정-
dc.contributor.author김명준-
dc.date.accessioned2022-11-29T02:32:46Z-
dc.date.available2022-11-29T02:32:46Z-
dc.date.issued2021-08-
dc.identifier.other31197-
dc.identifier.urihttps://dspace.ajou.ac.kr/handle/2018.oak/20357-
dc.description학위논문(박사)--아주대학교 일반대학원 :인공지능학과,2021. 8-
dc.description.tableofcontents1. Introduction 1 2. Representation of hierarchical networks 6 3. Semi-supervised learning for hierarchical networks: Classification 11 4. Semi-supervised learning for hierarchical networks: Regression 27 5. Applications of hierarchical networks 37 6. Conclusion 75 References 80-
dc.language.isoeng-
dc.publisherThe Graduate School, Ajou University-
dc.rights아주대학교 논문은 저작권에 의해 보호받습니다.-
dc.titleSemi-Supervised Learning for Hierarchical Networks-
dc.typeThesis-
dc.contributor.affiliation아주대학교 일반대학원-
dc.contributor.department일반대학원 인공지능학과-
dc.date.awarded2021. 8-
dc.description.degreeDoctoral-
dc.identifier.localId1227094-
dc.identifier.uciI804:41038-000000031197-
dc.identifier.urlhttps://dcoll.ajou.ac.kr/dcollection/common/orgView/000000031197-
dc.subject.keywordSemi-supervised learning-
dc.subject.keywordhierarchical networks-
dc.subject.keywordlabel propagation-
dc.subject.keywordnetwork-based Gaussian process-
dc.subject.keywordnetwork-based machine learning-
dc.description.alternativeAbstractA set of data can be obtained from different hierarchical levels in diverse domains, such as multi-levels of genome data in omics, domestic/global indicators in finance, ancestors/descendants in phylogenetics, genealogy, and sociology. Such layered structures are often represented as a hierarchical network. If a set of different data is arranged in such a way, then one can naturally devise a network-based learning algorithm so that information in one layer can be propagated to other layers through interlayer connections. Incorporating individual networks in layers can be considered as an integration in a serial/vertical manner in contrast with parallel integration for multiple independent networks. The hierarchical integration induces several problems on computational complexity, sparseness, and scalability because of a huge-sized matrix. In this dissertation, we propose semi-supervised framework of classification and regression for a hierarchically structured network. The proposed frameworks consists of naive and approximate versions, where trade-off between performance and time complexity exists. Furthermore, we show empirical performances of hierarchical network on various task along with some real-world applications including historical faction identification, disease co-occurrence prediction, and key gene identification for Dementia.-
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Graduate School of Ajou University > Department of Artificial Intelligence > 4. Theses(Ph.D)
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