首页|Jiangxi Normal University Reports Findings in Support Vector Machines (Applying support vector machines to a diagnostic classification model for polytomous attr ibutes in small-sample contexts)
Jiangxi Normal University Reports Findings in Support Vector Machines (Applying support vector machines to a diagnostic classification model for polytomous attr ibutes in small-sample contexts)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Support Vector Machine s is the subject of a report. According to news reporting from Nanchang, People’ s Republic of China, by NewsRx journalists, research stated, “Over several years , the evaluation of polytomous attributes in small-sample settings has posed a c hallenge to the application of cognitive diagnosis models. To enhance classifica tion precision, the support vector machine (SVM) was introduced for estimating p olytomous attribution, given its proven feasibility for dichotomous cases.” The news correspondents obtained a quote from the research from Jiangxi Normal U niversity, “Two simulation studies and an empirical study assessed the impact of various factors on SVM classification performance, including training sample si ze, attribute structures, guessing/slipping levels, number of attributes, number of attribute levels, and number of items. The results indicated that SVM outper formed the pG-DINA model in classification accuracy under dependent attribute st ructures and small sample sizes. SVM performance improved with an increased numb er of items but declined with higher guessing/slipping levels, more attributes, and more attribute levels.”
NanchangPeople’s Republic of ChinaAs iaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mac hines
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