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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 신현정 | - |
| dc.contributor.author | 김명준 | - |
| dc.date.issued | 2021-08 | - |
| dc.identifier.other | 31197 | - |
| dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/20357 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :인공지능학과,2021. 8 | - |
| dc.description.tableofcontents | 1. 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.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Semi-Supervised Learning for Hierarchical Networks | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 인공지능학과 | - |
| dc.date.awarded | 2021. 8 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.uci | I804:41038-000000031197 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000031197 | - |
| dc.subject.keyword | Semi-supervised learning | - |
| dc.subject.keyword | hierarchical networks | - |
| dc.subject.keyword | label propagation | - |
| dc.subject.keyword | network-based Gaussian process | - |
| dc.subject.keyword | network-based machine learning | - |
| dc.description.alternativeAbstract | A 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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