In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.
本文讨论了利用半监督学习方法进行视频语义标注的问题。
At present, most of video semantic annotation methods are based on statistic theory. The methods use supervised learning method to do semantic label.
目前已有的视频语义标注方法多是基于统计学理论，采用全监督学习方法进行语义标注工作。
So, the semi-supervised learning method by learning a small number of labeling samples and a large number of samples to establish classifier came into being.
如此，通过对少量已标记样本和大量未标记的样本进行学习从而建立分类器的半监督学习方法应运而生。
This paper uses a corpus with break indices based on C-TOBI. Applying supervised learning method, some useful attempts are made in the field of automatic break indices intonation.
本文采用了一个基于CTOBI的停顿指数标注的语料库，利用有指导的学习方法对自动停顿指数标注方面做了一些有益的探索。
How to make the effect of semi-supervised learning closed to or the same to the supervised learning by unlabeled samples information is the key of semi-supervised learning method.
如何利用未标记的视频样本信息达到类似于全监督学习的效果是半监督学习方法的关键。
If the ultimate effect of the semi-supervised method is the same or close to the result of supervised learning method, the semi-supervised learning is more advantages in labor costs and achievement.
若其最终的学习效果与全监督学习方法的效果一致或接近，则在人工成本和实现上，半监督学习方法更具有优越性。
In this paper, we present a method of training a feedforward neural network using supervised learning scheme to balance an inverted pendulum and cart system.
本文将专家在平衡—模拟倒摆小车时记录下来的数据经处理后，用监督式学习的方法训练一前置式神经网络。
In this paper, a new method which combines unsupervised and supervised learning strategy is put forward to construct the multi classification decision tree.
提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法。
A 3d expression generating method based on morphing and supervised learning is introduced.
提出一种基于变形和监督式学习的三维表情生成方法。
Presents a method of training a feedforward neural network using supervised learning scheme to balance an inverted pendulum and cart system.
采用平衡的倒摆小车所记录下来的数据，经处理后用有师学习方法来训练前馈神经网络。
The method of product feature extraction and analysis can be divided into supervised machine learning methods, semi-supervised machine learning algorithms and unsupervised machine learning algorithm.
产品评价对象的提取与分析的方法主要分为有监督的机器学习方法、半监督的机器学习算法、无监督的机器学习算法。
The method of product feature extraction and analysis can be divided into supervised machine learning methods, semi-supervised machine learning algorithms and unsupervised machine learning algorithm.
产品评价对象的提取与分析的方法主要分为有监督的机器学习方法、半监督的机器学习算法、无监督的机器学习算法。
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