使用模拟切削算法的SVM增量学习机制

提出使用模拟切削算法的SVM增量学习机制。模拟切削算法在核函数映射的特征空间中计算每个样本的预期贡献率， 仅选取预期贡献率较高的样本参与SVM增量学习， 有效解决传统SVM增量学习代价高、目标样本选取准确性低、分类器缺乏鲁棒性的问题。一个样本的预期贡献率采用通过该样本的映射目标的合适分离面对两类样本的识别率来表示。对目标样本的选取酷似果蔬削皮的过程， 所提算法由此得名。基准数据实验表明， 文中算法在学习效率和分类器泛化性能上具有突出优势。在有限资源学习问题上的应用表明该算法在大规模学习任务上的良好性能。

SVM Incremental Learning Using Simulated Cutting Algorithm

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.