[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116919-en":3,"doc-seo-116919-105":30,"detail-sidebar-cat-0-en-105":92},{"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},116919,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Anderson Acceleration For Bioinformatics-Based Machine Learning","Anderson acceleration (AA) is used to speed up convergence of iterative algorithms, yet its impact on classical machine learning classifiers remains insufficiently analyzed. This study examines tabular bioinformatics data, where classical models often outperform deep learning but lack thorough convergence analysis. A support vector machine (SVM) variant is built with Anderson acceleration, then evaluated on multiple biology-domain datasets. Results show AA substantially improves convergence and reduces training loss as iterations increase, motivating further work on applying AA to classical ML.","Anderson Acceleration For Bioinformatics-Based Machine Learning  \nSarwan Ali1,†, Prakash Chourasia1,† and Murray Patterson1, *  \n1 Georgia State University, Atlanta, USA  \narXiv :2302 .00347v2 [ cs .LG] 24 Aug 2023  \nAbstract  \nAnderson acceleration (AA) is a well-known method for accelerating the convergence of iterative algorithms with applications in various fields, including deep learning and optimization. Despite its popularity in these areas, the effectiveness of AA in classical machine learning classifiers has not been thoroughly studied. Tabular data, in particular, presents a unique challenge for deep learning models, and classical machine learning models are known to perform better in these scenarios. However, the convergence analysis of these models has received limited attention. To address this gap in research, we implement a support vector machine (SVM) classifier variant incorporating AA to speed up convergence. We evaluate the performance of our SVM with and without Anderson acceleration on several datasets from the biology domain and demonstrate that the use of AA significantly improves convergence and reduces the training loss as the number of iterations increases. Our findings provide a promising perspective on the potential of Anderson acceleration in training simple machine learning classifiers and underscore the importance of further research in this area. By showing the effectiveness of AA in this setting, we aim to inspire more studies that explore the applications ofAA in classical machine learning.  \nKeywords  \nAnderson Acceleration, SVM, Sequence Analysis  \n1. Introduction  \nAnderson acceleration is a method that can be used to enhance the convergence of gradient descent algorithms. Based on the difference between the current and prior weight vectors, a correction term is added to the weight vector updates at each iteration. When the gradients are changing quickly, or the optimization landscape is very non-convex, this correction term can aid in reducing oscillations and speeding convergence. Consider the optimization issue as a trajectory in the weight space, where the weight vector reflects the position at each iteration, to appreciate this concept better. Without Anderson acceleration, the gradients at each location alone control the trajectory of the optimization process. While Anderson acceleration can smooth out the trajectory and minimize oscillations, the trajectory is also affected by the difference between the current and prior weight vectors.  \nSolving the convex problem in finding gradient descent is a typical problem in optimization. Newton’s methods use the inverse Hessian matrix [1] to accelerate gradient descent, and they are successful in achieving a faster rate of convergence compared to gradient descent or accelerated gradient descent, but it is very expensive. By utilizing knowledge of the curvature of the loss function  \nKDH@IJCAI’23: Knowledge Discovery From Healthcare Data, August 20, 2023, Macao, S.A.R  \n* Corresponding author.  \n†  \nThese authors contributed equally.  \n$ [sali85@student.gsu.edu](sali85@student.gsu.edu) (S. Ali); [pchourasia1@student.gsu.edu](pchourasia1@student.gsu.edu)[ ](pchourasia1@student.gsu.edu)(P. Chourasia); [mpatterson30@gsu.edu](mpatterson30@gsu.edu) (M. Patterson)  \n© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4 .0 International (CC BY 4 .0) .  \nCEURWorkshopProceedings http://ceurISSN 1613-ws-0073.org CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \nlandscape and quasi-Newton algorithms [2] that compute a low-rank approximation of the Hessian, it is feasible to accelerate the training of machine learning(ML) models. The approximate replacement matrix for the Hessian Inverse can be found using quasi-Newton methods, described in detail by authors in [3] . The alternative option is to use the existing data that is already available for the fixed point approach, such as natural gradien","cbCaiseJ43Cfm7Kd","https://ap.wps.com/l/cbCaiseJ43Cfm7Kd","pdf",1457690,1,7,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Anderson acceleration and convergence\n## Relation to optimization methods\n## Motivation for studying classical ML classifiers\n# Proposed approach (AA-regularized SVM)","[{\"question\":\"What problem does Anderson acceleration address in iterative algorithms?\",\"answer\":\"Anderson acceleration improves convergence of iterative algorithms by adding a correction term derived from differences between current and prior weight vectors, which can reduce oscillations and speed up convergence.\"},{\"question\":\"How does this work evaluate Anderson acceleration for classical machine learning classifiers?\",\"answer\":\"The study implements an Anderson-accelerated SVM classifier variant and compares SVM training with and without AA across several biology-domain datasets.\"},{\"question\":\"What impact does Anderson acceleration have on training loss and convergence?\",\"answer\":\"Using Anderson acceleration significantly improves convergence and reduces training loss as the number of iterations increases, supporting its effectiveness for simpler classical ML models in this setting.\"}]","Anderson Acceleration For Bioinformatics-Based Machine Learning | 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problem does Anderson acceleration address in iterative algorithms?","Question",{"text":76,"@type":77},"Anderson acceleration improves convergence of iterative algorithms by adding a correction term derived from differences between current and prior weight vectors, which can reduce oscillations and speed up convergence.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does this work evaluate Anderson acceleration for classical machine learning classifiers?",{"text":81,"@type":77},"The study implements an Anderson-accelerated SVM classifier variant and compares SVM training with and without AA across several biology-domain datasets.",{"name":83,"@type":74,"acceptedAnswer":84},"What impact does Anderson acceleration have on training loss and convergence?",{"text":85,"@type":77},"Using Anderson acceleration significantly improves convergence and reduces training loss as the number of iterations increases, supporting its effectiveness for simpler classical ML models in this 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