基于概率和间隔信息的产品设计知识线性分类方法

Design Knowledge Linear Classification of Complex Product Based on Margin and Probability Information

摘要:

针对复杂产品设计知识类别数多、数据量大的分类问题，基于最小最大概率机的概率信息和样本间隔信息，使用线性的方法解决多分类问题。用样本的均值和协方差代替真实的均值和协方差，利用间隔信息和概率信息，构造加权投票矩阵，编码矩阵会随着不同的方法和不同的训练数据有不同的变化，同时也利用所携带的幅值信息，选出最大值的对应类别。线性分类器作为二类分类器可以加快处理速度。提出以子空间角度差异度量来表征各个类别概念间的差异，并通过对叉车产品设计知识的数值实验证明了该方法的有效性和实用性。

Abstract:

For the complex product design knowledge with a large number catalogs and data volumes, the multi-classification problem was solved by using linear approach based on the margin and probability information of MPM. The true mean and covariance were replaced by the sample ones. The weight voting matrix was established, the coding matrix varied with the difference algorithm and training data. The classifier with max value was selected by using the amplitude value. Linear classifier was used as binary one for high speed. The angle difference measurement of subspace was proposed to characterize the difference of every class conception. The feasibility and practicality of the above mentioned method were approved by forklift product design knowledge.