Imperial College London
The Support Vector Machine (SVM) is a popular supervised learning method, providing high accuracy in many classification and regression tasks. However, its training phase is a computationally expensive task. In this paper, the authors focus on the acceleration of this phase and a geometric approach to SVM training based on gilbert's algorithm is targeted, due to the high parallelization potential of its heavy computational tasks. The algorithm is mapped on two of the most popular parallel processing devices, a graphics processor and an FPGA device.