| 1. | Supervised learning of heuristic function for refutation
反演启发函数的监督学习算法 |
| 2. | The former belongs to supervised learning and the latter belongs to unsupervised learning
它们分属于有监督学习与无监督学习。 |
| 3. | A semi - supervised learning system was proposed based on art ( adaptive resonance theory )
摘要根据自适应谐振理论提出了半监督学习自适应谐振理论系统。 |
| 4. | 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络与回归的使用来探讨监督式学习。 |
| 5. | The distinct difference between supervised learning and unsupervised learning lies in whether the example consists of the pre - processed output value
这两种方法最大的区别就在于学习样本是否包含有预先规定好的输出值。 |
| 6. | It also proposes a method of supervised learning to train the decision function and provides the corresponding method of calculation to realize it
提出了一种通过监督学习来训练判别函数的方法，并给出了相应的实现算法。 |
| 7. | As we all know , the methods of feature selection for supervised learning perform pretty well with strong practice and simple operation
众所周知,在有指导学习环境下,出现了很多性能优越、实用性强和操作方便的属性选择方法。 |
| 8. | According to various of applications of the datasets , feature selection algorithms can be categorized as either supervised learning or unsupervised learning feature selection approaches
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。 |
| 9. | Classification is a sort of supervised learning ( i . e . , the learning of the model is " supervised " in that it is told to which class each training sample belongs )
需要指出的是：分类是一种有指导的学习（即模型的学习在被告知每个训练样本属于哪个类的“指导”下进行） 。 |
| 10. | The typical ones include relief - f , information gain and chi - square etc . feature selection was considered as feature selection in supervised learning from traditional view
其中的典型代表有relief - f 、信息增益和卡方检验等。过去传统意义上的属性选择通常是指在有指导学习环境下的属性选择。 |
