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中文摘要：
针对非线性问题,提出了基于支持向量机分类基础的先分类、再回归的预测方法。根据实际需要和专业知识先将样本集进行分类,判别测试样本的类别后,再利用回归算法预测测试样本的值。利用这一算法进行粮食产量预测,并与其他模型预测结果相比,准确度远优于其他产量预测方法。实验说明：先分类、再回归得到的拟合值比直接利用回归得到的拟合值要精确。
英文摘要：
For non-linear problem, the forecasting technique of pre-classification and later regression was proposed, based on the classification approach of Support Vector Machine （ SVM） . According to the actual requirements and professional knowledge, the sample cluster was classified first to decide the types of the test samples. Next the values of the test samples were forecast with the regression algorithm. Compared with other forecasting techniques and their forecasting results, this algorithm outperforms others in grain output prediction. The findings of the experiment show that the fitted value obtained from the forecasting technique of pre-classification and later regression is much more accurate than that from regression.
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