semi-supervised的例句大全

Graph-based semi-supervised learning algorithm is drawing lots of attention in recent years due its some internal characteristics.

半监督学习中的基于图的算法由于自身的一些特殊性质，越来越引起人们的关注。

Semi-Supervised Learning Algorithm Based on SVM and by Gradual Approach

基于支持向量机的渐近式半监督式学习算法

We can expand the indicators by using the semi-supervised method and the complementary knowledge of linguistics, that is, from lexeme to word cluster.

使用半指导的方法和语言学上的不同的词位集合构成词群的补充性的知识对指示词进行语义扩展计算，得到增强作用的指示词。

binary classifications are much faster than structural search of word alignment, which makes it possible to perform semi-supervised learning efficiently.

与结构化的搜索方法相比，通过一系列的二元分类决策来完成词对齐任务提高了搜索的效率，使得大规模数据上的半监督学习变得可能。

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。

Semi-supervised clustering based on spectral clustering

一种基于谱聚类的半监督聚类方法

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.

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。

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

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。

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。

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。

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。

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。

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。

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。

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。

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。

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。

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。

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。