Building Semantic Kernel for Persian Text Classification with a Small Amount of Training Data

The original idea of semantic kernels is to use semantic features instead of terms appeared in the text document. In this article, the documents are transformed into a new k-dimensional feature space by applying Singular Value Decomposition on the Term-Document matrix and extracting

کلیدواژه ها:

Semantic Kernel ، Vector Space Kernel ، Support Vector Machine ، Dimensionality Reduction ، Text Classification