Transductive Regression for Data With Latent Dependence Structure
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Goernitz, Nico | - |
dc.contributor.author | Lima, Luiz Alberto | - |
dc.contributor.author | Varella, Luiz Eduardo | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Nakajima, Shinichi | - |
dc.date.accessioned | 2021-09-02T09:53:18Z | - |
dc.date.available | 2021-09-02T09:53:18Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-07 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/74851 | - |
dc.description.abstract | Analyzing data with latent spatial and/or temporal structure is a challenge for machine learning. In this paper, we propose a novel nonlinear model for studying data with latent dependence structure. It successfully combines the concepts of Markov random fields, transductive learning, and regression, making heavy use of the notion of joint feature maps. Our transductive conditional random field regression model is able to infer the latent states by combining limited labeled data of high precision with unlabeled data containing measurement uncertainty. In this manner, we can propagate accurate information and greatly reduce uncertainty. We demonstrate the usefulness of our novel framework on generated time series data with the known temporal structure and successfully validate it on synthetic as well as real-world offshore data with the spatial structure from the oil industry to predict rock porositiesfrom acoustic impedance data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | MIXTURES | - |
dc.title | Transductive Regression for Data With Latent Dependence Structure | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1109/TNNLS.2017.2700429 | - |
dc.identifier.scopusid | 2-s2.0-85019840188 | - |
dc.identifier.wosid | 000436420400007 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.29, no.7, pp.2743 - 2756 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | - |
dc.citation.volume | 29 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 2743 | - |
dc.citation.endPage | 2756 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | MIXTURES | - |
dc.subject.keywordAuthor | Conditional random fields (CRFs) | - |
dc.subject.keywordAuthor | non-independent and identically distributed (IID) | - |
dc.subject.keywordAuthor | ridge regression (RR) | - |
dc.subject.keywordAuthor | semisupervised and transductive learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.