统计机器学习的三种分类方法z z z有序(Sequence)、无序(Non-Sequence)有指导(Supervised) 、无指导(Unsupervised)估计(Estimation)、非估计(Non-Estimation)z 有序和无序 ?
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无目标的，无指导的没有目标或目的的；无引导的。
属性选择问题可以分为有指导学习环境下的选择和无指导学习环境下的选择。
According to various of applications of the datasets, feature selection algorithms can be categorized as either supervised learning or unsupervised learning feature selection approaches.
基于NBM的无指导词义消歧正确率略低，但有很好的扩展性，值得进一步的研究。
And the unsupervised WSD based on NBM got a little lower precision in comparison to the supervised, but it is worthy further researching since it has a well extension performance.
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