Semisupervised Incremental Support Vector Machine Learning Based on Neighborhood Kernel Estimation

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.

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