Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Support Vector Machines are presented in a new report. According to news reporting originating in Xi’an, People’s Republic of China, by NewsRx journalists, research stated,“In this article, we study the support vector machine with interaction effects. The pliable lasso penalty, which allows for estimating the main effects of the covariates X and the interaction effects between the covariates and a set modifiers Z is implemented to handle the interaction effect.” The news reporters obtained a quote from the research from Northwest University, “Interaction variables are included in a hierarchical manner by first considering whether their corresponding main effect variables have been included in the model to avoid over-fitting. The loss function employed is the squared hinge loss, with the pliable lasso penalty and then, the block-wise coordinate descent approach is employed.” According to the news reporters, the research concluded: “The results from the simulation and real data show the effectiveness of the pliable lasso in building support vector machine models in situations where interaction effects are involved.” This research has been peer-reviewed. For more information on this research see: Pliable Lasso for the Support Vector Machine. Commu- nications in Statistics - Simulation and Computation, 2024;53(2):1-13. Communications in Statistics - Simulation and Computation can be contacted at: Taylor & Francis Inc, 530 Walnut Street, Ste 850, Philadelphia, PA 19106, USA.
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