首页|Study Findings from Chongqing Normal University Provide New Insights into Suppor t Vector Machines (Absolute Value Inequality SVM for the PU Learning Problem)
Study Findings from Chongqing Normal University Provide New Insights into Suppor t Vector Machines (Absolute Value Inequality SVM for the PU Learning Problem)
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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 new report. According to news reporting from Chongqing, Pe ople's Republic of China, by NewsRx journalists, research stated, "Positive and unlabeled learning (PU learning) is a significant binary classification task in machine learning; it focuses on training accurate classifiers using positive dat a and unlabeled data."Financial supporters for this research include Chongqing Municipal Government. Our news correspondents obtained a quote from the research from Chongqing Normal University: "Most of the works in this area are based on a two-step strategy: t he first step is to identify reliable negative examples from unlabeled examples, and the second step is to construct the classifiers based on the positive examp les and the identified reliable negative examples using supervised learning meth ods. However, these methods always underutilize the remaining unlabeled data, wh ich limits the performance of PU learning. Furthermore, many methods require the iterative solution of the formulated quadratic programming problems to obtain t he final classifier, resulting in a large computational cost. In this paper, we propose a new method called the absolute value inequality support vector machine , which applies the concept of eccentricity to select reliable negative examples from unlabeled data and then constructs a classifier based on the positive exam ples, the selected negative examples, and the remaining unlabeled data."
Chongqing Normal UniversityChongqingPeople's Republic of ChinaAsiaMachine LearningSupport Vector Machines
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