一种基于聚类的支持向量机增量学习算法

提出了一种基于聚类的支持向量机增量学习算法.先用最近邻聚类算法将训练集分成具有若干个聚类子集,每一子集用支持向量机进行训练得出支持向量集;对于新增数据首先聚类到相应的子集,然后计算其与聚类集内的支持向量之间的距离,给每个训练样本赋以适当的权重;而后再建立预估模型.此算法通过钢材力学性能预报建模的工业实例研究,结果表明:与标准的支持向量回归算法相比,此算法在建模过程中不仅支持向量个数明显减少,而且模型的精度也有所提高.

A sort of support vector machine incremental learning algorithm based on clustering

A sort of incremental learning algorithm for support vector machine based on clustering was proposed. The nearest neighbor clustering algorithm was used for separating a whole training data set into several clusters, and each cluster subset was trained by support vector machine to obtain the support vector subset. The new sample data was firstly clustered in a certain subset. Then the distances between the new sample data and the support vectors of the cluster subset were calculated to weight every support vector. Finally, a new weighed model was formed with these samples. The proposed method was applied to a practical case of modeling prediction ability of the mechanical properties of steel materials. Comparing with the traditional support vector regression algorithm, this proposed method demonstrates its advantages of the smaller number of support vectors and the better generalization capability.