Semisupervised Incremental Support Vector Machine Learning Based on Neighborhood Kernel Estimation
提出一种基于邻域核估计的半监督增量支持向量机算法，通过核回归估计未标记数据并引入增量学习，在人工数据集和青霉素发酵过程基准测试中验证了有效性。
Semisupervised scheme has emerged as a popular strategy in the machine learning community due to the expensiveness of getting enough labeled data. In this paper, a semisupervised incremental support vector machine (SE-INC-SVM) algorithm based on neighborhood kernel estimation is proposed. First, kernel regression is constructed to estimate the unlabeled data from the labeled neighbors and its estimation accuracy is discussed from the analogy with tradition RBF neural network. The incremental scheme is derived to improve the learning efficiency and reduce the computing time. Simulations for manual data set and industrial benchmark-penicillin fermentation process demonstrate the effectiveness of the proposed SE-INC-SVM method.