Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created.

监督学习是最常见的分类问题，因为目标往往是让计算机去学习我们已经创建好的分类系统。

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Supervised learning with the use of regression and classification networks with sparse data sets will be explored.

也将在课程中以带有稀疏值理论的分类神经网路与回归的使用来探讨监督式学习。

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In this paper, a new method which combines unsupervised and supervised learning strategy is put forward to construct the multi classification decision tree.

提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法。

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