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.
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。
In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.
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。
A semi-supervised learning system was proposed based on ART (adaptive resonance theory).
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。
Therefore, this algorithm has solved the problem of model updating in semi-supervised learning under appropriate conditions.
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。
Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.
如何利用未标记的视频样本信息达到类似于全监督学习的效果是半监督学习方法的关键。
How to make the effect of semi-supervised learning closed to or the same to the supervised learning by unlabeled samples information is the key of semi-supervised learning method.
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。
The ANN can derive a continuous-spread stability index to indicate the relative stability degree by means of a semi-supervised learning algorithm.
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。
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.
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。
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.
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。
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.
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。
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.
基于图的学习是近几年来半监督学习中一个相当活跃的方向，它用图来描述样本空间，利用近邻点的位置来控制标记信息的传播。
Graph-based learning is a very active direction of semi-supervised learning in recent years. It describes the sample space by graph, and USES neighbors to spread label information in point cloud.
基于图的学习是近几年来半监督学习中一个相当活跃的方向，它用图来描述样本空间，利用近邻点的位置来控制标记信息的传播。
Graph-based learning is a very active direction of semi-supervised learning in recent years. It describes the sample space by graph, and USES neighbors to spread label information in point cloud.
应用推荐