The idea of supervised learning is to provide examples for an algorithm to learn from.
所谓的监督式学习就是为算法提供学习的例子。
Reinforcement learning has the ability to learn from experience as opposed to supervised learning which learns from examples.
与监督学习从范例中学习的方式不同，强化学习不需要先验知识，而是具有从经验中学习的能力。
Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.
半监督学习算法同时考虑有标记和无标记数据，能显著提升学习效果。
The technical term for this is supervised learning, and I think we just haven't figured out the right ideas yet for the other types of learning.
用术语来说，这种方式叫监督学习，我认为到目前为止，我们还没有找到其他学习类型的正确思路。
Other feedforward models and supervised learning models.
其它前馈型网络模型和监督学习模型；
Lattice machine is a novel approach to supervised learning.
格机是一种新颖的有监督学习方法。
If you want to use some supervised learning algorithm, you need labeled data.
如果你想使用有监督的学习算法，你需要标记数据。
What are the differences between supervised learning and reinforcement learning?
监督学习与强化学习的区别是什么？
A 3d expression generating method based on morphing and supervised learning is introduced.
提出一种基于变形和监督式学习的三维表情生成方法。
The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory.
这个课程的重点将放在与统计学习理论架构中有关的监督式学习问题。
In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.
本文讨论了利用半监督学习方法进行视频语义标注的问题。
However, the result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.
但是，无指导学习环境下的属性选择往往无法取得像有指导学习环境下那样令人满意的结果。
Therefore, this algorithm has solved the problem of model updating in semi-supervised learning under appropriate conditions.
该算法在一定条件下解决了半监督学习环境下的模型更新问题。
Unsupervised learning is used to adjust input weight values and supervised learning is utilized to adjust output weight values.
学习过程中，采用无监督学习算法对输入权重进行调整，采用有监督学习算法对输出权重进行调整。
The comparison and analysis of geometric information's impact on semi-supervised learning is performed through the experiments.
最后通过实验对比和分析了几种几何信息对半监督学习效果的影响。
That's a very active area of machine-learning research, but it's not a solved problem to the extent that supervised learning is.
这是机器学习研究的一个非常活跃的领域，但目前研究的进展与监督式学习还是不能比拟的。
However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.
然而，传统的监督学习算法需要标记大量的训练样本来建立满意的分类器。
The ANN can derive a continuous-spread stability index to indicate the relative stability degree by means of a semi-supervised learning algorithm.
使用一个半监督学习算法，ANN可产生一个能够指示相对稳定度的连续分布的暂态稳定指标。
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。
So, the semi-supervised learning method by learning a small number of labeling samples and a large number of samples to establish classifier came into being.
如此，通过对少量已标记样本和大量未标记的样本进行学习从而建立分类器的半监督学习方法应运而生。
Semi-supervised learning methods including self-training and co-training were shown in the task of PPI on how to alleviate the tag burden as much as possible.
使用半监督学习方法中的自训练、协同训练方法，利用少量已标注样本和大量未标注样本来完成蛋白质关系抽取的任务。
A decade ago, it was thought that supervised learning was the way to do this: a team of linguists created dictionaries and grammar rules and taught them to the computer.
十年前，人们认为监督式学习是实现它的方法：一组语言学家造出字典，语法规则，然后，把它们教给计算机。
The learning of connectionism, which consists mainly of supervised learning, intensive learning and unsupervised learning, is modelled after the learning of human beings.
其学习是对人类学习的模拟，主要有监督学习、强化学习和无监督学习三种。
Feedback is provided not from of a teaching process as in supervised learning, but as punishments and rewards in the environment. Example problems are systems and robot control.
反馈并不像监督学习那样来自于训练的过程，而是作为环境的惩罚或者是奖赏。
An adaptive nearest neighbor locally linear embedding algorithm is proposed to overcome this shortage. Experiment results show that the algorithm ADAPTS well the supervised learning problems.
针对这个缺点，提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法，数值实验证明算法对于有监督的学习问题，具有较好的适应性。
If the ultimate effect of the semi-supervised method is the same or close to the result of supervised learning method, the semi-supervised learning is more advantages in labor costs and achievement.
若其最终的学习效果与全监督学习方法的效果一致或接近，则在人工成本和实现上，半监督学习方法更具有优越性。
Semi-Supervised Learning : Input data is a mixture of labelled and unlabelled examples.
半监督学习：输入数据由带标记的和不带标记的组成。
Semi-Supervised Learning : Input data is a mixture of labelled and unlabelled examples.
半监督学习：输入数据由带标记的和不带标记的组成。
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