This paper mainly focus on the text classification algorithms.
本文研究文本的自动分类算法。
Recently, for the study of Text Automatic classification technology, researchers mostly focus on the exploration and improvement of different classification algorithms.
目前，对于文本分类技术的研究，大多数研究者的精力主要放在各种不同分类方法的探索与改进上。
Secondly, the text studies the Statistical Learning Theory(STL) and Support Vector Machine(SVM)theory seriously, discusses multi-category classification algorithms of SVM.
其次，认真研究了统计学习理论的主要内容和SVM算法的基本原理，并且就SVM的多种多类别分类算法分别加以讨论。
The algorithms and models presented in this dissertation will be valuable for future studies in text classification and other fields in text processing.
这些算法和模型对今后研究文本分类以及其它文本处理问题将有很大的参考价值和借鉴作用。
We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.
提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。
We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.
提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。
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