[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123558-en":3,"doc-seo-123558-105":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},123558,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine learning with geo, temporal, textual, and visual data for real world applications","The thesis investigates how machine learning can be applied to real-world problems using multimodal data spanning geographic (geo), temporal, textual, and visual signals. It develops and evaluates methods that align heterogeneous representations and improve predictive performance in practical settings. Research outputs focus on robust modeling strategies that support data integration, enabling more accurate analysis and decision-making. The work is positioned within the Multimedia Information Retrieval For Business Lab at the University of Amsterdam and reflects research conducted with institutional and city-level support.","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, P.O. Box 19185, 1000 GD 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:26 Apr 2026  \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.","cbCairGcViiYDaZq","https://ap.wps.com/l/cbCairGcViiYDaZq","pdf",54886151,1,178,"English","en",105,"# Thesis information and publication metadata\n## Academic context and affiliations\n# Research focus\n## Machine learning with geo, temporal, textual, and visual data\n# Contributions and evaluation\n## Real-world application orientation\n# Supplementary materials\n## Rights, disclaimers, and publication notice","[{\"question\":\"What kinds of data does the thesis focus on?\",\"answer\":\"The thesis focuses on geographic (geo), temporal, textual, and visual data. It studies how these signals can be combined for real-world use cases.\"},{\"question\":\"What is the main research direction of the document?\",\"answer\":\"The main direction is applying machine learning to real-world applications using multimodal data. The work emphasizes integrating heterogeneous representations to improve practical performance.\"},{\"question\":\"Where was the research conducted?\",\"answer\":\"The research was carried out at the University of Amsterdam, within the Multimedia Information Retrieval For Business Lab, and includes support for the project as stated in the publication metadata.\"}]","Machine learning with geo, temporal, textual, and visual data for real world applications | PDF",1785817312,449,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-with-geo-temporal-textual-and-visual-data-for-real-world-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-with-geo-temporal-textual-and-visual-data-for-real-world-applications/123558/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What kinds of data does the thesis focus on?","Question",{"text":75,"@type":76},"The thesis focuses on geographic (geo), temporal, textual, and visual data. 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