中文摘要：

产生式方法和判别式方法是解决分类问题的两种不同框架，具有各自的优势．为利用两种方法各自的优势，文中提出一种产生式与判别式线性混合分类模型，并设计一种基于遗传算法的产生式与判别式线性混合分类模型的学习算法．该算法将线性混合分类器混合参数的学习看作一个最优化问题，以两个基分类器对每个训练数据的后验概率值为数据依据，用遗传算法找出线性混合分类器混合参数的最优值．实验结果表明，在大多数数据集上，产生式与判别式线性混合分类器的分类准确率优于或近似于它的两个基分类器中的优者．

英文摘要：

The generative approaches and the discriminative approaches are two kinds of paradigms for solving classification problems. To exploit the advantages of these approaches, a linear hybrid of generative/ discriminative model （LHGD） is proposed, and a learning algorithm of LHGD based on genetic algorithms （LHGD_GA） is designed. LHGD_GA regards hybrid parameter learning of the linear hybrid classification model as an optimization problem, and utilizes genetic algorithms to find the best hybrid parameters of linear hybrid classification model. The experimental results show that the linear hybrid generative/discriminative classifier is better than or similar to the better classifier of two base classifiers on most datasets.