关键词: 哈萨克语文本分类, SVM, 特征选择, KNN

Abstract: This paper introduced the basic theory of the Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) algorithm and two different features selection methods in Kazak natural language. An empirical study of using the SVM, kNN, Bayes algorithm to categorize the Kazak text was conducted. The experimental results show that compared with kNN, Bayes, SVM has better categorization of the Kazak text. Due to the characteristics of Kazak's morpheme and configuration, the precision and recall will be lowered if the word is cut with affix.

Key words: Kazak text categorization, SVM, featrur selection, KNN