unlabeled

In recent years, semi-supervised learning has become a research focus in the machine learning field, Semi-supervised learning taking advantage of labeled samples and unlabeled samples to guide the learning process, leading to better learning performance.

近年来，半监督学习已经成为机器学习领域的一个研究热点。半监督学习同时利用标记样本和无标记样本来指导学习过程，从而获得更好的学习性能。

SVM has been recently used in many applications. We deeply research the Semi-supervised Learning, machine learning and the problems of current intrusion detection system. The research in this paper provides new methods based on SVM for solving mass unlabeled data.

本文在深入研究半监督学习和传统的机器学习理论及目前入侵检测系统所面临问题的基础上，选择SVM方法作为基础分类算法，提出了一种新的处理海量未标记数据的半监督算法。

In the process, we calculate the posterior probability of semantics by unlabeled samples information.

在计算的过程中, 使用了未标记样本的信息计算语义出现的后验概率.

the proposed method began by choosing some instances from unlabeled dataset that are most similar to one labeled example in labeled dataset, and assigning them the corresponding ranker.

基于最近邻，针对每个有标签样本，在无标签数据集选择与其最近似的若干样本赋以相同序数；

Meanwhile, RCAS employs semi-supervised learning and relevance feedback to deal with the unlabeled samples in the related class and unrelated class.

同时，RCAS算法结合了机器学习中半监督学习和相关反馈算法的优点，对有关类和无关类中未标记样本采用不同的方式加以利用。

Unlabeled probe genotyping for detection of single nucleotide polymorphism of IL-6 promoter

无标记探针技术检测IL-6基因启动子单核苷酸多态性

Semi-supervised learning is the primary method of making use of unlabeled samples for learning, and is a very active research field in machine learning.

半监督学习（semi-supervised learning）是利用未标记样本进行学习的主流技术，是目前机器学习中非常活跃的研究方向之一。

It is very easy to obtain a large number of unlabeled examples, but for a large number of labeled ones is very difficult.

获取大量无标记数据比较简单，而获取大量有标记数据非常困难。

The new algorithm is to firstly divide unlabeled data into many subsets with a new label by k-means clustering, then train the SVMs using the new data set to get decision boundary and support vectors, at last use the SVMs classifier to classify the unlabeled data.

首先利用k均值算法将未知数据划分成某个数量的子集，然后对新数据进行支持向量机训练得到决策边界与支持矢量，最后对无标识数据进行分类。

Semi-supervised clustering is to use a small amount of labeled samples and unlabeled samples to guide the clustering process.

半监督聚类也就是无监督学习，就是使用少量的标记样本对无标记样本的聚类过程进行指导。

Typically, for each class, the most confident unlabeled sequence, together with its predicted label, is added to the labeled training set.

通常，将每类中置信度最高的未标记序列及其预测的类别添加到训练集中。

Unlabeled luggage is liable to be lost.

未作标记的行李很容易丢失.