首页|Studies from University of Munster in the Area of Support Vector Machines Descri bed (Polynomial Kernel Learning for Interpolation Kernel Machines With Applicati on To Graph Classification)
Studies from University of Munster in the Area of Support Vector Machines Descri bed (Polynomial Kernel Learning for Interpolation Kernel Machines With Applicati on To Graph Classification)
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2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Support Vector Machines is now ava ilable. According to news reporting from Munster, Germany, by NewsRx journalists , research stated, "Since all training data is interpolated, interpolating class ifiers have zero training error. However, recent work provides compelling reason s to investigate these classifiers, including their significance for ensemble me thods." Financial supporters for this research include China Scholarship Council, German Research Foundation (DFG), European Union (EU). The news correspondents obtained a quote from the research from the University o f Munster, "Interpolation kernel machines, which belong to the class of interpol ating classifiers, are capable of good generalization and have proven to be an e ffective substitute for support vector machines, particularly for graph classifi cation. In this work, we further enhance their performance by studying multiple kernel learning. To this end, we propose a general scheme of polynomial combined kernel functions, employing both quadratic and cubic kernel combinations in our experimental work. Our findings demonstrate that this approach improves perform ance compared to individual graph kernels."
MunsterGermanyEuropeEmerging Techn ologiesMachine LearningMathematicsPolynomialSupport Vector MachinesVec tor MachinesUniversity of Munster
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