semi-supervised

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。

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。

Research on Domain-Specific Term Extraction Based on Semi-Supervised Learning

基于半监督学习的特定领域术语抽取算法的研究

Propose a semi-supervised feature selection algorithm based on cost sensitive Laplacian SVM ( FS-CSLapSVM).

提出了基于代价敏感Laplacian SVM的半监督特征选择算法FS-CSLapSVM。

Research on Graph-based Semi-Supervised Learning Model and Classifier Design

基于图的半监督学习模型研究与分类器设计

Behavior Modeling and Abnormality Detection Based on Semi-Supervised Learning Method

基于半监督学习的行为建模与异常检测

In this thesis, we introduce a new semi-supervised learning algorithm: Semi-supervised LPP algorithm, which efficiently combines the characters of both SVM and LPP.

本论文提出了一种有效地结合SVM和LPP的半监督学习算法&LPP半监督算法。

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。

It uses semi-supervised learning algorithm based on FCM, and for feature selection.

该方案使用的算法就是基于FCM的半监督学习算法，同时可以进行特征选择。

A useful ending-criterion is proposed for semi-supervised BP algorithm, and applied it to transient stability classification of power systems.

针对用于BP网络训练的半监督学习算法，提出了一种新的实用的训练结束判据，并将其应用于电力系统暂态稳定分类中。

Semi-supervised learning and its applications to fingerprint image segmentation.

半监督学习及其在指纹图像分割上的应用研究。

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.

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。

Based on different base classifier algorithms, experiments evaluate performance on three semi-supervised learning algorithms including Self-Training, Co-Training and Tri-Training.

对三种半监督学习算法Self-Training、Co-Training和Tri-Training结合不同的基分类器通过实验来进行性能评估。

Furthermore, we extend ASC for semi-supervised cases and propose a semi-supervised adaptive spectral clustering ( Semi-ASC) and a semi-supervised spectral ensemble clustering algorithm.

在此基础上，将该方法扩展到半监督领域，提出了一种半监督自适应谱聚类算法（Semi-ASC）和半监督谱聚类集成算法。

Usually, such problems are also known as semi-supervised learning.

通常这样的学习问题也被称为半监督学习。

As people make an intensive study of SVM in the primal space, various problems meted in application were solved in the primal space, such as the problem of semi-supervised learning.

随着人们在原始空间对支持向量机研究的深入，实际应用中碰到的各种问题也开始在原始空间进行求解，如半监督学习问题等。

A Semi-Supervised Learning Algorithm Based on Lie Group and Application

一种基于李群的半监督学习算法及应用研究

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。

Experiment results prove that semi-supervised learning is more efficient than traditional HMM in syllable recognition.

实验结果表明，在小样本情况下，半监督学习方法比传统的HMM方法具有更高的识别率。

Absorbing the main thoughts of semi-supervised learning, PC_SEM is presented.

融入半监督学习的思想，提出了PC SEM算法。

Semi-supervised learning to data-driven fault diagnosis has been introduced.

尝试研究了半监督学习在基于数据驱动的故障诊断方法中的应用。

Propose a semi-supervised learning algorithm based on ensemble learning.

提出了一种基于分类器集成的半监督学习算法。

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。

Multi-Modal Web Search Query Refinement Based on Semi-Supervised Learning

一种基于半监督学习的多模态Web查询精化方法

Semi-supervised clustering based on spectral clustering

一种基于谱聚类的半监督聚类方法

Study of Semi-Supervised Learning-Based and Support Vector Machine-Based Eyebrow Recognition

基于半监督学习和支持向量机的眉毛识别方法研究

Inspired by the theory of simple random walk, we propose a unified framework for semi-supervised learning, i.e. multilevel random walk ( MRW). MRW can produce numerous multi-class label propagation algorithms by constructing various component random walk models based on prior knowledge.

在简单随机游走理论的启发下，我们提出一个统一的半监督学习框架&多层随机游走，通过构建各种组件级的随机游走模型，可以产生一系列对应的多分类标签传播算法。

The experimental evaluations show that the proposed asymmetric semi-supervised learning methodology in conjunction with random subspace theory is effective to improve CBIR performance and achieves better performance than some existing approaches.

实验结果表明，不对称的半监督学习策略及随机子空间理论可有效地改进SVM反馈性能，且所提出算法明显优于同类SVM反馈方法。

The algorithm is based on Bayesian decision machinery, with estimation of the structure from probability density function, for two-class problems. ( 3) The paper gives out a semi-supervised learning algorithm based on FCM.

该算法基于贝叶斯决策理论，通过概率密度函数进行分布估计，对两类别半监督学习问题进行判定。（3）给出了一个基于FCM的半监督学习算法。

In this thesis, I focused on graph-based semi-supervised learning.

本文主要研究的是基于图的半监督学习方法与应用。