使用无监督学习算法对标签数据。
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.
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。
However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.
现在我已经做了对的监督和无监督学习算法，如决策树，一些基本的阅读聚类，神经网络等。
Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks... etc.
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.
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。
Unsupervised learning is used to adjust input weight values and supervised learning is utilized to adjust output weight values.
局部保持投影(LPP)是一种新的数据降维技术，但其本身是一种非监督学习算法，对于分类问题效果不是太好。
Locality Preserving Projections algorithm (LPP) is a new dimensionality reduction technique. But it is an unsupervised learning algorithm. It could not process classification effectively.
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.
而后选择了一种在特征空间进行非监督学习的算法，对彩色图像进行了分割，实验证明该方法识别率很高。
Then it studies segment color images in the feature space to choose a method which bases on non-supervise, and the experiments have testified the efficiency.
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.
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.
随机树分类算法是一种有监督学习的模式识别分类算法，可有效地应用于增强现实系统中的特征识别与匹配。
Randomized tree is a supervised classification algorithm for pattern recognition, which can be effectively used in augmented reality feature recognition and matching.
随机树分类算法是一种有监督学习的模式识别分类算法，可有效地应用于增强现实系统中的特征识别与匹配。
Randomized tree is a supervised classification algorithm for pattern recognition, which can be effectively used in augmented reality feature recognition and matching.
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