A GPU accelerated hybrid GA-SVM for large scale datasets: Cu-GA-SVM - Musa PEKER; Osman ÖZKARACA (Gazi Üniversitesi Fen Bilimleri Dergisi, 2018)
In this study, CUDA based speed optimization of a hybrid method consisting of Genetic Algorithm and Support Vector Machines has been performed. In machine learning, it is aimed to achieve high accuracy values from the developed methods. It is also a target for the proposed algorithm to work quickly while finding the results. In this study, speed parameter which is indispensable especially in real time applications is taken into consideration and a new GPU technology is used to classify the data quickly. Therefore, CUDA programming, which allows us to program on graphics processors of which importance and use are increasing in recent years, has been benefited from. Support vector machine optimized by genetic algorithm has been used as the classification algorithm. The experiments have been performed on a computer with NVIDIA GeForce 940MX graphics card, which consists of 384 CUDA core. Experiments performed on large scale data sets have shown that CUDA programming has positive effects on the results. In this way, the infrastructure of a quick system for real-time applications can be created by using the graphics processors in the classification phase of the machine learning applications.