Correlative component analysis ( cca ) was used to eliminate multicollinearity and noise of original sample data before classifying by svm . to improve the classification performance of svm and obtain the optimal discriminative function , the ega proposed in this work was used to optimize the parameters of svm including correlative components ( ccs ) , penalty factor c , and kernel width factor 因此,为了充分利用支持向量机良好的分类能力,使之能处理存在复杂相关关系的观测数据,给出了结合分类相关成分分析( cca )的支持向量机建立分类模型的方法( cca - svm ) ,又利用本文第三章所提出的ega算法优化分类相关成分数及支持向量机参数。