[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124178-en":3,"doc-seo-124178-105":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":20,"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},124178,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Stochastic Methods for Machine Learning and their Applications - Doctor of Philosophy Dissertation","Stochastic Methods for Machine Learning and their Applications presents a PhD dissertation focused on probabilistic and stochastic mechanisms for improving machine learning algorithms. The work introduces stochastic induction for decision trees, including Haar tree constructions, and develops analysis covering computational complexity and relationships to objective functions. It further proposes a synthetic reduced nearest-neighbor leveraging neural networks, supported by convergence arguments and pruning, followed by enhancements using two-layer neural networks for image classification.","UC Merced  \nUC Merced Electronic Theses and Dissertations  \nTitle  \nStochastic Methods for Machine Learning and their Applications  \nPermalink  \n[https://escholarship.org/uc/item/14m2h5gn](https://escholarship.org/uc/item/14m2h5gn)  \nAuthor  \nAlizadeh, Azar  \nPublication Date  \n2024  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA, MERCED  \nStochastic Methods for Machine Learning and their Applications  \nA dissertation submitted in partial satisfaction of the requirements for the degree  \nDoctor of Philosophy  \nProposal for PhD qualifying exam in  \nElectrical Engineering and Computer Science  \nby  \nAzar Alizadeh  \nCommittee members:  \nProfessor Mukesh Singhal, Chair  \nProfessor Reza Ehsani  \nProfessor Xiaoyi Lu  \nCopyright Notice  \n©2024 Azar Alizadeh  \nAll Rights Reserved.  \nThe Dissertation of Azar Alizadeh is approved, and it is acceptable in quality and form for publication on microfilm and electronically:  \nReza Ehsani  \nXiaoyi Lu  \nMukesh Singhal (Chair)  \nUniversity of California, Merced  \n2024  \nDedication  \nThis thesis is dedicated, first and foremost, to my parents Shamsi Hashemnejhad and Ali Alizadeh, whose unwavering support, love, and sacrifices have made this journey possible. Your belief in me and your guidance through every step of life has been my foundation.  \nTo my beloved daughter Diana Naserkhaki, who fills my life with joy and purpose and whose presence has been a constant source of inspiration and strength. You were by my side during the toughest moments, and together, we have grown and learned, shaping our paths forward.  \nTo my siblings, Arezou, Mohammad, and Azadeh Alizadeh, for being my closest allies and for standing by me in every challenge, always encouraging me and giving me a sense of belonging.  \nThis work is also dedicated to my advisors, mentors, and colleagues, whose guidance and insights have shaped my research and scholarly growth. Your wisdom and support have been instrumental in my academic journey.  \nFinally, I dedicate this thesis to all those whose names may not be mentioned but whose contributions, large and small, have left an indelible mark on my life and academic endeavors. I deeply appreciate your belief in me and your encouragement along the way.  \nEpigraph  \nThe growth of knowledge depends entirely upon disagreement.  \n— Karl Popper  \nContents  \nList of Figures ix  \nList of Tables xi  \nList of Algorithms xii  \nAcknowledgment xiii  \nVita and Publication xiv  \nAbstract xvii  \n1 Introduction and Background 1  \n1. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.2 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.3 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 14  \n1.4 Stochastic Processes in Machine Learning . . . . . . . . . . . . . . . . 18  \n1.5 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21  \n1.6 Motivation and Objectives . . . . . . . . . . . . . . . . . . . . . . . . 23  \n1.7 Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . 24  \n2 Stochastic Induction of Decision Trees with Application to Learning  \nHaar Trees 26  \n2. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27  \n2.2 Problem Statement and Basic Idea . . . . . . . . . . . . . . . . . . . 30  \n2.3 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32  \n2.3. 1 Induction of a decision tree . . . . . . . . . . . . . . . . . . . 32  \n2.4 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33","cbCain3CfqvbXTlJ","https://ap.wps.com/l/cbCain3CfqvbXTlJ","pdf",4356358,1,164,"English","en",105,"# Contents\n## 1 Introduction and Background\n## 2 Stochastic Induction of Decision Trees with Application to Learning Haar Trees\n## 3 A Novel Approach For Synthetic Reduced Nearest-Neighbor Leveraging Neural Networks\n## 4 Enhancing Synthetic Reduced Nearest-Neighbor with Two-Layer Neural Networks: A Step Forward in Image Classification","[{\"question\":\"What is the dissertation's main topic?\",\"answer\":\"The dissertation centers on stochastic methods for machine learning and their applications, covering both theory and algorithmic designs.\"},{\"question\":\"How are decision trees handled in the work?\",\"answer\":\"It presents stochastic induction of decision trees, with a specific focus on Haar tree constructions and related experiments and analysis.\"},{\"question\":\"What neural-network approaches are proposed for synthetic reduced nearest-neighbor?\",\"answer\":\"It proposes a synthetic reduced nearest-neighbor framework leveraging neural networks, and then enhances it with two-layer neural networks for improved image classification performance.\"}]","Stochastic Methods for Machine Learning and their Applications - 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