[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123256-en":3,"doc-seo-123256-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123256,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning with geo, temporal, textual, and visual data for real world applications","This thesis presents machine learning methods for real-world applications by integrating four data modalities: geographic (geo), temporal, textual, and visual signals. It focuses on learning from heterogeneous, often noisy and incomplete information, aiming to improve model effectiveness in practical settings. The work is developed within the University of Amsterdam research environment, supported by relevant lab activities and institutional backing. The resulting contributions target better representations and predictive performance for tasks grounded in real conditions.","UvA-DARE (Digital Academic Repository)  \nMachine learning with geo, temporal, textual, and visual data for real world applications  \nSukel, M. M.  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nSukel, M. M. (2024) . Machine learning with geo, temporal, textual, and visual data for real world applications. [Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:28 May 2025  \nMachine learning with geo, temporal, textua l , and visua l data for rea l wor ld applications  \nMachine learning  \nwith geo, temporal, textual, and visual data for real world applications  \nMaarten Sukel  \nMaa rten Sukel  \nMachine Learning with Geo, Temporal, Textual, and Visual Data for Real World Applications  \nMaarten Sukel  \nAuthor: Maarten Sukel  \nPromotor: dr. Stevan Rudinac  \nPromotor: prof. dr. Marcel Worring  \nCover illustration: Mei-li Nieuwland  \nTypography cover: Frank Westenberg  \nPrint: Ridderprint  \nCopyright © 2024 by Maarten Sukel  \nAll rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission from the author.  \nISBN 978-94-6506-456-7  \nMachine Learning with Geo, Temporal, Textual, and Visual Data for Real  \nWorld Applications  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor  \naan de Universiteit van Amsterdam  \nop gezag van de Rector Magnificus  \n[prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteitop vrijdag 8 november 2024, te 11:00 uur  \ndoor Maarten Michiel Sukel  \ngeboren te Voorburg  \nPromotiecommissie  \nPromotores: dr. S. Rudinac prof. dr. M. Worring  \nOverige leden: prof. dr. E. Kanoulas prof. dr. C.G.H. Diks  \nprof. dr. C.I.M. Nevejan  \nprof. dr. C. Gurrindr. P.S.M. Mettes  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam Dublin City University Universiteit van Amsterdam  \nFaculteit Economie en Bedrijfskunde  \nThe work described in this thesis has been carried out within the Faculty of Science and Faculty of Economics and Business at the Multimedia Information Retrieval For Business Lab of the University of Amsterdam. The research is partly supported by the City of Amsterdam.  \nvi  \nTo a world where we all live in harmony with our garbage, food, machines, and  \nfellow humans.","cbCaidAvW3ldZA4t","https://ap.wps.com/l/cbCaidAvW3ldZA4t","pdf",54873674,1,178,"English","en",105,"# Thesis overview\n## Data modalities and real-world application focus\n## Research context and support","[{\"question\":\"What kinds of data does the thesis focus on?\",\"answer\":\"The thesis focuses on geo (geographic) data, temporal data, textual data, and visual data, and on how to learn from them together for real-world tasks.\"},{\"question\":\"What is the main goal of the research?\",\"answer\":\"The goal is to develop machine learning approaches that work effectively on heterogeneous real-world information and improve predictive performance in practical applications.\"},{\"question\":\"Where was the thesis research conducted and supported?\",\"answer\":\"The work was carried out at the University of Amsterdam within the Multimedia Information Retrieval For Business Lab, with part of the research supported by the City of Amsterdam.\"}]","Machine learning with geo, temporal, textual, and visual data for real world applications | 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