摘要
为进一步提高SVM增量训练的速度,在有效保留含有重要分类信息的历史样本的基础上,对当前增量训练样本集进行了约简,提出了一种基于类边界壳向量的快速SVM增量学习算法,定义了类边界壳向量。算法中增量训练样本集由壳向量集和新增样本集构成,在每一次增量训练过程中,首先从几何角度出发求出当前训练样本集的壳向量,然后利用中心距离比值法选择出类边界壳向量后进行增量SVM训练。分别使用人工数据集和UCI标准数据库中的数据进行了实验,结果表明了方法的有效性。
To reduce the computational cost of the incremental learning,a fast Support Vector Machine（SVM） incremental learning algorithm is proposed,and the definition of the between-class convex hull vector is given.The given algorithm is based on utilizing the result of the previous training effectively and retaining the most important samples for the incremental learning to reduce the computational cost.In the process of incremental learning,the convex hull vectors of the previous training and the newly added samples constitute the current training sample set,the current training sample set is pre-extracted from the geometric point of view by using the convex hulls algorithm,the central distance ratio method is used to obtain the between-class convex hull vectors before the SVM incremental training.Experiments prove that the given algorithm has better classification performance.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第23期185-187,248,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.50505051
陕西省自然科学研究计划No.2007F19~~
关键词
支持向量机
增量学习
壳向量
Support Vector Machine（SVM）
incremental learning
convex hull vector
