[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117337-en":3,"doc-seo-117337-105":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},117337,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Lifelong Machine Learning With Adaptive Resonance Theory - Doctoral Dissertation","This publication-option dissertation investigates lifelong machine learning using Adaptive Resonance Theory (ART) algorithms. Lifelong learning (L2) formalizes continual and incremental learning while addressing catastrophic forgetting, where agents overwrite prior knowledge when facing novel information without appropriate regularization. ART resolves the stability-plasticity dilemma by assigning learning to existing categories or instantiating new knowledge when inputs are sufficiently novel. The work also examines how deep neural networks’ feature learning can be vulnerable to catastrophic forgetting, motivating adaptations of ART from vision and biomedical data to deep network formulations.","Scholars' Mine  \n\n| Doctoral Dissertations | Student Theses and Dissertations |\n| --- | --- |\n| Spring 2025\u003Cbr>Lifelong Machine Learning With Adaptive Resonance Theory\u003Cbr>Sasha Alexander Petrenko\u003Cbr>Missouri University of Science and Technology\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/doctoral_dissertations](https://scholarsmine.mst.edu/doctoral_dissertations)\u003Cbr> Part of the Electrical and Computer Engineering Commons\u003Cbr>Department: Electrical and Computer Engineering |  |\n\nRecommended Citation  \nPetrenko, Sasha Alexander, \"Lifelong Machine Learning With Adaptive Resonance Theory\" (2025) . Doctoral Dissertations. 3373.  \n[https://scholarsmine.mst.edu/doctoral_dissertations/3373](https://scholarsmine.mst.edu/doctoral_dissertations/3373)  \nThis thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nLIFELONG MACHINE LEARNING WITH ADAPTIVE RESONANCE THEORY  \nby  \nSASHA ALEXANDER PETRENKO  \nA DISSERTATION  \nPresented to the Graduate Faculty of the  \nMISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY  \nIn Partial Fulfillment of the Requirements for the Degree  \nDOCTOR OF PHILOSOPHY  \nin  \nCOMPUTER ENGINEERING  \n2024  \nApproved by:  \nDr. Donald C. Wunsch II, Advisor  \nJoe Stanley  \nJonathan Kimball  \nDaryl Beetner  \nHank Pernicka  \nCopyright 2024  \nSASHA ALEXANDER PETRENKO  \nAll Rights Reserved  \niii  \nPUBLICATION DISSERTATION OPTION  \nThis dissertation consists of the following three articles, formatted in the style used by the Missouri University of Science and Technology.  \nPaper I: Pages 4-62 have been accepted by the journal MDPI Information in 2024 for its special issue titled The Resonant Brain: A Themed Issue Dedicated to Professor Stephen Grossberg.  \nPaper II: Pages 63-102 will be submitted to IEEE Transactions on Systems, Man, and Cybernetics: Systems.  \nPaper III: Pages 103-145 have been submitted to the Elsevier journal Neural Networks.  \niv  \nABSTRACT  \nThis publication option dissertation is composed of three papers concerning the study of the problem lifelong machine learning with Adaptive Resonance Theory (ART) algorithms. Lifelong learning (L2) is a challenging machine learning paradigm that both encompasses and formalizes the fields of continual learning and incremental learning. The field is concerned with the mitigation of the phenomenon of catastrophic forgetting whereby learning agents that are faced with incrementally novel information deleteriously overwrite previous knowledge if that learning process is not regularized to counteract this consequence. ART algorithms solve the this stability-plasticity dilemma by optimally assigning learning to categories or instantiating new knowledge when information is sufficiently novel. While the appeal of deep neural networks is their capacity to learning useful feature manifolds simultaneously with the task at hand, they are especially subject to catastrophic forgetting both by their hierarchical architectures and by the current techniques used to train them.  \nThe publications of this dissertation explore techniques for adapting ART algorithms from novel application domains in computer vision and biomedical data analysis to formulations of deep learning networks that complement and combine the unique strengths of adaptive resonance and deep learning to tackle the lifelong machine learning problem.  \nv  \nACKNOWLEDGMENTS  \nThis completion of this dissertation was only possible through the support, confidence, and patience of a myriad of persons and agencies.  \nI would like to begin by thanking my doctoral advisor Dr. Donald C. Wunsch II for the resources, support, and opportunity to pursue this degree; he has been an admirable and steadfast advisor, and I hope ","cbCaind2UzlFnqpV","https://ap.wps.com/l/cbCaind2UzlFnqpV","pdf",7856142,1,179,"English","en",105,"# Publication Dissertation Option\n# Abstract\n# Acknowledgments\n# List of Illustrations\n# List of Tables","[{\"question\":\"What problem does this dissertation focus on?\",\"answer\":\"It focuses on lifelong machine learning with Adaptive Resonance Theory (ART) algorithms, especially mitigating catastrophic forgetting during continual and incremental learning.\"},{\"question\":\"How do ART algorithms address the stability-plasticity dilemma?\",\"answer\":\"ART assigns learning to existing categories when possible, or creates instantiations of new knowledge when the information is sufficiently novel.\"},{\"question\":\"What are the three papers included in the publication-option dissertation?\",\"answer\":\"Paper I (accepted in MDPI Information, 2024) covers the special issue theme; Paper II (planned for IEEE Transactions on Systems, Man, and Cybernetics: Systems) and Paper III (submitted to Elsevier Neural Networks) address related lifelong learning techniques.\"}]","Lifelong Machine Learning With Adaptive Resonance Theory - Doctoral Dissertation | PDF",1785675258,451,{"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},"lifelong-machine-learning-with-adaptive-resonance-theory-doctoral-dissertation","",{"@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/lifelong-machine-learning-with-adaptive-resonance-theory-doctoral-dissertation/117337/",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","2026-08-02",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 problem does this dissertation focus on?","Question",{"text":75,"@type":76},"It focuses on lifelong machine learning with Adaptive Resonance Theory (ART) algorithms, especially mitigating catastrophic forgetting during continual and incremental learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do ART algorithms address the stability-plasticity dilemma?",{"text":80,"@type":76},"ART assigns learning to existing categories when possible, or creates instantiations of new knowledge when the information is sufficiently novel.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the three papers included in the publication-option dissertation?",{"text":84,"@type":76},"Paper I (accepted in MDPI Information, 2024) covers the special issue theme; 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