In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data.
可以简单的理解为：非监督学习是指，尝试从未标注的数据中，寻找隐藏的结构。
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。
Due to the supervised view of point, most of the present tensor dimensionality reduction methods cannot take full advantage of the unlabeled data.
现有的张量维数约简方法大都是监督的，它们不能有效利用未标签样本数据的信息。
The problem of combining a small set of labeled data with a large pool of unlabeled data for text classification task has been extensively studied.
采用少量已标记和大量未标记文档进行文本分类已成为一种重要研究趋势。
This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples.
该方法利用大量的未标识数据进行有效聚类，并将聚类结果用于小样本情形下的支持向量机分类。
In order to solve the problem existing in training data sets, present Bayes algorithm is im - proved and an algorithm using unlabeled data to improve the capability of the classifier is proposed.
为了解决该方法存在的训练数据集问题，本文改进了现有的贝叶斯分类算法，提出了利用未标记数据提高贝叶斯分类器性能的方法。
In order to solve the problem existing in training data sets, present Bayes algorithm is im - proved and an algorithm using unlabeled data to improve the capability of the classifier is proposed.
为了解决该方法存在的训练数据集问题，本文改进了现有的贝叶斯分类算法，提出了利用未标记数据提高贝叶斯分类器性能的方法。
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