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。
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。
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。
For learning document classification on line, the paper gives the semi-supervised learning fuzzy ART model (SLFART) based on adaptive resonance theory and the models algorithm.
为了对在线学习文档进行分类，本文根据自适应谐振理论给出了一个半监督学习模糊art模型(SLFART)及其算法。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
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