首页|Research from University of Texas Tyler Yields New Study Findings on Machine Lea rning (Machine Learning Application of Generalized Gaussian Radial Basis Functio n and Its Reproducing Kernel Theory)
Research from University of Texas Tyler Yields New Study Findings on Machine Lea rning (Machine Learning Application of Generalized Gaussian Radial Basis Functio n and Its Reproducing Kernel Theory)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting out of Tyler, Texas, by NewsR x editors, research stated, "Gaussian Radial Basis Function Kernels are the most -often-employed kernel function in artificial intelligence for providing the opt imal results in contrast to their respective counterparts." Funders for this research include Office of Dean; Office of Research, Scholarshi p, And Sponsored Programs; Robert R. Muntz Library At The University of Texas.The news reporters obtained a quote from the research from University of Texas T yler: "However, our understanding surrounding the utilization of the Generalized Gaussian Radial Basis Function across different machine learning algorithms, su ch as kernel regression, support vector machines, and pattern recognition via ne ural networks is incomplete. The results delivered by the Generalized Gaussian R adial Basis Function Kernel in the previously mentioned applications remarkably outperforms those of the Gaussian Radial Basis Function Kernel, the Sigmoid func tion, and the ReLU function in terms of accuracy and misclassification. This art icle provides a concrete illustration of the utilization of the Generalized Gaus sian Radial Basis Function Kernel as mentioned earlier."
University of Texas TylerTylerTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachi ne Learning
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