Findings on Machine Learning Discussed by Investigators at University of Sultan Moulay Slimane ( an Efficient Primal-dual Method for Solving Non-smooth Machine Learning Problem )

摘要

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting originating from Beni Mellal, Morocco, by NewsRx correspondents, research stated, “This paper deals with the machine learning model as a framework of regularized loss minimization problem in order to obtain a generalized model. Recently, some studies have proved the success and the efficiency of nonsmooth loss function for supervised learning problems Lyaqini et al. [1].” Financial support for this research came from Universite de Nantes. Our news editors obtained a quote from the research from the University of Sultan Moulay Slimane, “Motivated by the success of this choice, in this paper we formulate the supervised learning problem based on L 1 fidelity term. To solve this nonsmooth optimization problem we transform it into a mini-max one. Then we propose a Primal-Dual method that handles the mini-max problem. This method leads to an efficient and significantly faster numerical algorithm to solve supervised learning problems in the most general case. To illustrate the effectiveness of the proposed approach we present some experimental-numerical validation examples, which are made through synthetic and real-life data.” According to the news editors, the research concluded: “Thus, we show that our approach is outclassing existing methods in terms of convergence speed, quality, and stability of the predicted models.” This research has been peer-reviewed. For more information on this research see.