Abstract: A new batch processing incremental learning method based on support vector machines is proposed to improve the model accuracy and update the model for the increasing batch data in the real work. The proposed method has been applied to a practical case of modeling prediction ability of mechanical property of steel materials. Compared with the traditional support vector machine incremental learning algorithm, the obtained model results demonstrate this promising method improves the model accuracy.

Key words: Support vector regression, Batch processing, Incremental learning

摘要： 针对生产实际中数据批量增加的情况，为了提高所建立的模型准确性和模型更新问题，提出了一种基于支持向量回归的批处理增量学习方法。算法通过对钢材力学性能预报建模的工业实例进行研究，结果表明，与传统的支持向量机增量学习算法相比，提高了模型的精度，具有良好的应用潜力。

关键词: 支持向量回归, 批处理, 增量学习

CLC Number: 

• TP18