The data sets have features such as high-dimensional, sparseness and binary value in many clustering applications.
在许多聚类应用中,数据对象是具有高维、稀疏、二元的特征。
The hierarchical clustering method is applied to deal with the problem that the solution of MLS-SVM is lack of sparseness.
运用谱系聚类方法解决多核最小二乘支持向量机的解缺乏稀疏性的问题。
Using pseudowords we can overcome data sparseness problem in supervised WSD and fully verify the experimental effect of word sense classifier.
使用伪词可以避免有指导的词义消歧方法中的数据稀疏问题,充分验证词义分类器的实验效果。
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