Abstract A method named Simulated Cutting Algorithm (SCA) is introduced for SVM incremental learning. SCA computes the anticipated contribution for the mapped target of each training sample in feature space mapped by a kernel function, and then chooses samples with higher anticipated contribution for SVM incremental learning. It effectively solves the problems in traditional incremental learning, such as higher training cost, lower accuracy for selecting target samples and lacking robustness. The anticipated contribution rate of a sample is indicated by the recognition rate towards two classes of samples of an appropriate separating hyperplane going through the mapped target of this sample point. Since the way for choosing target samples is very similar to that for paring garden stuff, the proposed algorithm acquires its name from this. Numerical experiments on benchmark datasets show the proposed method is superior in learning efficiency and generalization performance of a classifier. The application of the proposed algorithm in learning with limited resources demonstrates its excellent performance in large-scale learning tasks.
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2010, Vol. 23 
