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中文摘要：
针对产品销售时序包含噪声的数据特征,提出一种基于自适应分段损失函数的支持向量机模型（ASε-SVM）.ASε-SVM为每个样本点赋一个单独的不敏感损失值,以此降低模型对包含较大噪声的样本点的依赖性,并从理论上证明了该方法可增强模型部分的泛化性能.将ASε-SVM与ε-SVM共同应用于处理一个数值算例和一个汽车销售预测实例中,仿真实验结果表明,ASε-SVM是有效可行的,可获得比ε-SVM更精确的预测结果.
英文摘要：
Aiming at data characteristics of noise existing in the product sale series, a support vector machine based on the adaptive segmented loss function（ASε-SVM） is proposed. In the ASε-SVM, a separate insensitive loss value is assigned to each sample point adaptively, which can reduce the influence of inaccurate samples on the final model. It is proved in theory that the method can enhance partial generalization performance of the model. The ASε-SVM is applied to a numerical value example and the automobile sales forecasting in contrast with the ε support vector machine（ε-SVM）. The experiment results show that the ASε-SVM is effective and feasible, by which more accurate forecasting results are obtained over the ε-SVM.
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