摘要
增量学习广泛运用于人工智能、模式识别等诸多领域,是解决系统在训练初期样本量少而随时间推移性能降低的有效方法。本文针对经典支持向量机当训练样本数量多而运算速度较慢的缺点,在分析支持向量机的基础上,提出基于驱动错误准则的增量学习方法,实验结果表明,该算法不仅能保证学习机器的精度和良好的推广能力,而且算法的学习速度比经典的SVM算法快,可以进行增量学习。
Incremental learning is widely used in artificial intelligence, pattern recognition and other fields. It is an effective method to solve the problem that the efficiency of the system declines in the process of studying training samples which is of a small number in the beginning. For the disadvantage of the classical support vector machine getting slower when the number of training samples gets larger, this thesis proposes an incremental learning algorithm based on Drive error criterion. The experimental results show that this algorithm not only guarantees the precision and good generalization ability of the learning machine, but also faster than the classic SVM algorithm. Therefore, it can be used in incremental learning.
出处
《计算技术与自动化》
2012年第3期100-103,共4页
Computing Technology and Automation
基金
国防预研基金(9140A27020211JB3402)
关键词
机器学习
驱动错误准则
SVM
增量学习
machine learning
drive error criterion
SVM
incremental learning
