首页|Researchers from Massachusetts Institute of Technology Report Findings in Machin e Learning (Using Taylor-approximated Gradients To Improve the Frank-wolfe Metho d for Empirical Risk Minimization)
Researchers from Massachusetts Institute of Technology Report Findings in Machin e Learning (Using Taylor-approximated Gradients To Improve the Frank-wolfe Metho d for Empirical Risk Minimization)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Data detailed on Machine Learning have been presented. According to news originatingfrom Cambridge, Massachusetts, by NewsRx correspondents, research stated, “The Frank-Wolfe method has become incr easingly useful in statistical and machine learning applications due to the stru ctureinducingproperties of the iterates and especially in settings where linea r minimization over the feasible setis more computationally efficient than proj ection. In the setting of empirical risk minimization-one of thefundamental opt imization problems in statistical and machine learning-the computational effecti venessof Frank-Wolfe methods typically grows linearly in the number of data obs ervations n. This is in starkcontrast to the case for typical stochastic projec tion methods.”
CambridgeMassachusettsUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningMa ssachusetts Institute of Technology
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