基于核向量空间模型的专利分类

Patent Categorization Based on Kernel Vector Space Model

提出了一种利用核函数改进向量空间的新模型:核向量空间模型.该模型利用Mercer核,把输入空间的样本映射到高维特征空间,在高维特征空间中按向量空间模型操作,然后用核向量空间模型实现专利分类.理论分析及在专利分类中的实验表明,所提出的模型比经典向量空间模型有更高的正确分类率.

A novel model, namely, kernel vector space model, is established by using the kernel function to improve the vector space. In this model, the Mercer kernel is used to map the data in the original space to a high-dimenslonal feature space in which data can be identified as in the vector space. The proposed model is then applied to the to the patent categorization, with the theoretical and experimental results indicating its greater correctness than the traditional vector space model.