论文标题
用支持向量机模型的文本分类的培训精度降低
Training with reduced precision of a support vector machine model for text classification
论文作者
论文摘要
本文提出了使用量化对支持向量机（SVM）训练过程中多级文本分类效率的影响。这项工作的重点是将使用降低精度训练的SVM模型与其原始形式进行比较。使用量化的主要优点是在专用硬件平台上的计算时间和内存足迹中减少，该平台支持低精度计算（例如GPU（16位）或FPGA（任何位宽度））。本文提出了SVM培训过程对文本分类准确性的精确降低的影响。使用OpenMP库执行CPU的实现。此外，提出了使用双，单，半精度实现GPU的结果。
This paper presents the impact of using quantization on the efficiency of multi-class text classification in the training process of a support vector machine (SVM). This work is focused on comparing the efficiency of SVM model trained using reduced precision with its original form. The main advantage of using quantization is decrease in computation time and in memory footprint on the dedicated hardware platform which supports low precision computation like GPU (16-bit) or FPGA (any bit-width). The paper presents the impact of a precision reduction of the SVM training process on text classification accuracy. The implementation of the CPU was performed using the OpenMP library. Additionally, the results of the implementation of the GPU using double, single and half precision are presented.
