查询语句的优化就是对于给定的查询，选择代价最小的操作序列，使查询过程既节省时间，又节省空间。
The optimization of query sentences is to select operation sequence with minimal costs in terms of given query so as to make query a time-saving and space-saving process.
又以预测误差平方和SSE最小为目标，构造了优选并自动生成最佳平滑参数使平滑模型得以优化的最速下降算法，增强了指数平滑模型对时间序列的适应能力。
Aiming the square sum of error (SSE), we construct the algorithm to iterate and select an optimal parameter for optimizing the new models, which ADAPTS the model to time series more.
并从支持向量机的几何原理出发提出了一种基于序列最小最优化算法的改进算法，验证结果证明了该改进算法的正确性。
From the geometrical theory of SVM, this thesis advanced a new method that bases on SMO. The experiment proved this method to be correct.
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