[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122949-en":3,"doc-seo-122949-105":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},122949,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning Training Optimization Using the Barycentric Correction Procedure - Study","Machine learning models face a persistent limitation: long execution time in high-dimensional settings. This study combines multiple ML approaches with an efficient initialization methodology, the barycentric correction procedure (BCP), to accelerate training while maintaining predictive quality. Experiments use synthetic data and an educational dataset from a private university. Results show significant time improvements as instance counts and dimensionality increase without accuracy loss on both synthetic and real data. For very high-dimensional spaces, however, BCP with LinearSVC after an estimated Gaussian RBF feature map becomes computationally infeasible in both time and accuracy.","MACHINE LEARNING TRAINING OPTIMIZATION USING THE BARYCENTRIC  \nCORRECTION PROCEDURE  \nSofía Ramos-Pulido, Neil Hernández-Gress and Héctor G. Ceballos  \nCancino  \nTecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico,  \n64849 Monterrey, N.L  \nABSTRACT  \nMachine learning (ML) algorithms are predictively competitive algorithms with many humanimpact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.  \nKEYWORDS  \nSupport vector machine, neuronal networks, gradient boosting, barycentric correction procedure, synthetic data, linear separable cases, nonlinear separable cases, real data.  \n1. INTRODUCTION  \nArtificial neural networks and machine learning models have assumed paramount significance in numerous applications that profoundly affect human activities. These methodologies have gained prominence due to their exceptional predictive accuracy [1-6] . Neuronal networks, now named deep learning, re-emerged after 2010 due to massive improvements in computer resources, some innovations, and successful applications [7] . Support vector machines and gradient boosting stand as pillars in the field of machine learning [8-11] .  \nDeep learning has emerged as a cornerstone for tackling intricate tasks such as object recognition, speech recognition, and the development of autonomous vehicles [12] . Support vector machines have demonstrated their efficacy in domains like computer security, image categorization, and the extraction and recognition of soft biometrics [13] . Meanwhile, gradient boosting has found resounding success in a wide array of sectors, including finance [14] education [15], and the cryptocurrency realm [16], among countless others.  \nIn addition to their remarkable accuracy and proven success in various applications, Support Vector Machines (SVM) are renowned for their ability to construct intricate decision boundaries,  \neven when dealing with datasets featuring only a limited number of features [17] . One compelling theoretical aspect that distinguishes SVM from other algorithms is its convex objective function, guaranteeing the discovery of the optimal solution consistently [18] . Additionally, SVM possesses the unique characteristic of architecture self-determination, eliminating the need for prior definition.  \nTurning to the Gradient Boosting algorithm, as discussed in the work of [19], tree-based ensemble methods, like gradient boosting, offer interpretable outcomes with minimal data preprocessing requirements. The boosting family of algorithms has consistently ranked among the most accurate classifiers across a wide range of datasets [20] . Despite its sensitivity to noise and outliers, particularly in smaller datasets, it consistently exhibits lower testing error rates [20] .  \nThere is no denying the paramount importance, efficiency, and success stories associated with algorithms like neural networks, support vector machines, and gradient boosting, even when dealing with small databases. However, it is crucial to acknowledge that despite their many merits, all three methods face their share of challenges.  \nDeep learning, which employs backpropagation (BP), has encountered criticism due to certain theoretical lim","cbCaihoBbe1ygofE","https://ap.wps.com/l/cbCaihoBbe1ygofE","pdf",1234161,1,10,"English","en",105,"# Introduction\n## Motivation: accuracy and execution-time challenges\n## Overview of major ML models\n## Proposed solution using BCP","[{\"question\":\"What problem does the study address in machine learning training?\",\"answer\":\"It targets long execution times for machine learning in high-dimensional spaces.\"},{\"question\":\"How does the barycentric correction procedure (BCP) help training?\",\"answer\":\"BCP initializes algorithms using a refined subset of instances from the training set to improve convergence and reduce training overhead.\"},{\"question\":\"What were the main findings on time and accuracy?\",\"answer\":\"The combination of ML methods with BCP produced significant time benefits on both synthetic and real data as instances and dimensions increased, without losing accuracy.\"}]","Machine Learning Training Optimization Using the Barycentric Correction Procedure - 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