针对现有的多类分类算法效率低下的问题,提出一种K-means聚类算法和超球结合的多类分类算法.对每一类样本,先使用K-means算法获得子类;再在各个子类上构造最小超球,由此对每类都获得一个超球集;这些超球将样本空间分割,根据样本点所在空间的位置综合得到决策函数,用于对输入样本点进行类别判断.从理论上分析该方法能够有效提高分类的速度和准确率.%As current multi-class classification methods are low in efficiency, this paper gave a multi-class classification method based on K-means cluster and hyper-sphere. Firstly it used K-means cluster acquire the baby classes of every father class,then made hyper-sphere of every baby class. After this work, divided the text space up to every different areas. Aim at the position of text and these areas, fabricated decision-making function respectively which could classify a text to the correct class.The analyse given here clearly shows that this method can efficiently improve the speed and veracity of the clssifier.
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