关键词: 奇异值分解, 神经网络, 文本分类, BP算法, 级联相关算法

Abstract: A new text categorization model based on cascade neural network and Singular Value Decomposition（SVD） is proposed.The neural network is trained by the cascade-correlation algorithm.Most classic classification systems represent the contents of documents with a set of index terms，it has been known as Vector Space Model（VSM）.However，this method needs a high dimensional space to represent the documents，and it does not take into account the semantic relationship between terms，which can lead to poor classification performance.In this paper，SVD is used to learn and represent relations among very large numbers of words and very large numbers of natural text passages in which they occurred.It can not only greatly reduce the dimensional but also discover the important associative relationships between terms.The experiments show that it also helps to accelerate the training speed and improves the classification accuracy.

Key words: Singular Value Decomposition（SVD）, neural network, text categorization, BP algorithm, cascade-correlation algorithm