Text Categorization Algorithm Based on Manifold Learning and Support Vector Machines
基于非线性流形学习和支持向量机的文本分类算法

In order to solve the text classification problem, this paper put forward a text classification algorithm based on manifold learning and support vector machine (LLE-LSSVM). Firstly, high dimension text characteristics are reduced by LEE algorithm, and the inner rule and characteristics of the information are mined to obtain meaningful low-dimensional feature space. Secondly the features arc input into the I_SSVM to be learnt while using chaotic particle swarm algorithm to optimize LSSVM parameters. Lastly establishes the text classification model. The simulation results show that the proposed algorithm improves text classification accuracy and reduces the classification time, and it is an effective text classification algorithm.