首页|New Support Vector Machines Study Results Reported from Tianjin Agricultural Uni versity (Multi-task Support Vector Machine Classifier With Generalized Huber Los s)
New Support Vector Machines Study Results Reported from Tianjin Agricultural Uni versity (Multi-task Support Vector Machine Classifier With Generalized Huber Los s)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Support Vector Machines. According to news reporting from Tianjin, People’s Repu blic of China, by NewsRx journalists, research stated, “Compared to single-task learning (STL), multi-task learning (MTL) achieves a better generalization by ex ploiting domain-specific information implicit in the training signals of several related tasks. The adaptation of MTL to support vector machines (SVMs) is a rat her successful example.” The news correspondents obtained a quote from the research from Tianjin Agricult ural University, “Inspired by the recently published generalized Huber loss SVM (GHSVM) and regularized multi-task learning (RMTL), we propose a novel generaliz ed Huber loss multi-task support vector machine including linear and non-linear cases for binary classification, named as MTL-GHSVM. The new method extends the GHSVM from single-task to multi-task learning, and the application of Huber loss to MTL-SVM is innovative to the best of our knowledge. The proposed method has two main advantages: on the one hand, compared with SVMs with hinge loss and GHS VM, our MTL-GHSVM using the differentiable generalized Huber loss has better gen eralization performance; on the other hand, it adopts functional iteration to fi nd the optimal solution, and does not need to solve a quadratic programming prob lem (QPP), which can significantly reduce the computational cost.”
TianjinPeople’s Republic of ChinaAsi aEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mach inesTianjin Agricultural University
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