semi-supervised的例句大全

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。

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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。

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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。

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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

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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

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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

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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。

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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。

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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。

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Research of protein-protein interaction extraction based on semi-supervised learning

基于半监督学习的蛋白质关系抽取研究

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The goal of semi-supervised learning is to learn from partially labeled data.

半监督学习研究的目的是在整个数据集中只有一部分样本有标记的情况下，如何对数据进行分类。

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Study of Intrusion Detection Algorithm Based on Semi-supervised Learning

基于半监督学习的入侵检测算法研究

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Research on Expression Recognition Methods Based on Semi-Supervised Learning and Decision Level Fusion

基于半监督学习和决策融合的人脸表情识别方法研究

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Semi-supervised Learning and Its Application to Intrusion Detection System

半监督学习在入侵检测系统中的应用

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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

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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

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Semi-supervised learning flourishes as it can circumvent the limitations of unsupervised learning and supervised learning.

半监督学习的蓬勃发展规避了无监督学习和监督学习的局限性。

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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。

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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。

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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。

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