基于云模型的最接近支持向量机增量学习方法*

针对经典支持向量机在增量学习中的不足，提出一种基于云模型的最接近支持向量机增量学习算法。该方法利用最接近支持向量机的快速学习能力生成初始分类超平面，并与k近邻法对全部训练集进行约简，在得到的较小规模的精简集上构建云模型分类器直接进行分类判断。该算法模型简单，不需迭代求解，时间复杂度较小，有较好的抗噪性，能较好地体现新增样本的分布规律。仿真实验表明，本算法能够保持较好的分类精度和推广能力，运算速度较快。

Incremental PSVM learning algorithm based on cloud model

Aiming at the limitations of incremental learning in classical SVM, an incremental PSVM learning algorithm based on cloud model is proposed in this paper. The fast learning ability of PSVM is employed to yield the initial classification hyperplane, and then, all training datasets are reduced by using k-NN method and the plane. After that, we utilize cloud model to directly discriminate analysis on the reduced dataset. The simple algorithm, with less computational time and better anti-noise ability, could well embody the distribution of incremental samples and can be solved without iteration. Experiment results show that the algorithm could not only keep well classification accuracy and generalization ability, but also improve the training speed.