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small set of samples
相关语句
  小样本
     The results show that it has better performance than the other three classifier on the standard text sample set,and it has some superiority on small set of samples.
     结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器,在小样本分类上具有一定优势。
短句来源
     A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed,in a small set of samples.
     提出一种在小样本的情况下,基于多层贝叶斯网络的医学图像语义建模方法.
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  “small set of samples”译为未确定词的双语例句
     The method is composed of the following steps: (1) Dynamic time warping (DTW) based spectral clustering method is used to obtain a small set of samples to initialize the hidden Markov models (HMMs) of normal behaviors;
     该算法包括以下几个主要步骤:(1)通过基于动态时间归整(DTW)的谱聚类方法获取适量的正常行为样本,对正常行为的隐马尔可夫模型(HMM)进行初始化;
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  相似匹配句对
     Small.
     Small、为正品。
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     The set
     集合(英文)
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     set with (?)
     集且(?)
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     Set-up Scheme of the Small-Scale Lan
     小型局域网的组建方案
短句来源
     Gas monitor set in small coal mines
     中小型煤矿瓦斯监测系统的配置及更新
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  small set of samples
HPLC elution times also correlated with ester position in this small set of samples.
      
All the studies mentioned were done using a small set of samples.
      


A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. SVM focuses on the samples near the boundary in training time, and those samples intermixed in another class are usually no good to improve the classifier's performance, instead...

A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. SVM focuses on the samples near the boundary in training time, and those samples intermixed in another class are usually no good to improve the classifier's performance, instead they may greatly increase the burden of computation and their existence may lead to overlearning and decrease the generalization ability. In order to improve the generalization ability we present an improved SVM: NN-SVM. It first prunes the training set, reserves or deletes a sample according to whether its nearest neighbor has same class label with itself or not, then trains the new set with SVM to obtain a classifier. Experiment results show that NN-SVM is better than SVM in speed and accuracy of classification.

支持向量机 (SVM )是一种较新的机器学习方法 ,它利用靠近边界的少数向量构造一个最优分类超平面 .在训练分类器时 ,SVM的着眼点在于两类的交界部分 ,那些混杂在另一类中的点往往无助于提高分类器的性能 ,反而会大大增加训练器的计算负担 ,同时它们的存在还可能造成过学习 ,使泛化能力减弱 .为了改善支持向量机的泛化能力 ,该文在其基础上提出了一种改进的SVM———NN SVM :它先对训练集进行修剪 ,根据每个样本与其最近邻类标的异同决定其取舍 ,然后再用SVM训练得到分类器 .实验表明 ,NN SVM相比SVM在分类正确率、分类速度以及适用的样本规模上都表现出了一定的优越性

Based on the application of artificial immune network memory classifier(AINMC) in data classification,it presents a way of using the classifier to solve text classification.It is compared with two kinds of classifier,which is based on the principle of immunity,and traditional Bayesian classification.The results show that it has better performance than the other three classifier on the standard text sample set,and it has some superiority on small set of samples.

论文在人工免疫记忆分类器用于解决数据分类的基础上,提出将该分类器用于解决文本分类的方法。与两种基于免疫原理的文本分类方法和传统的贝叶斯分类器进行了比较研究。结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器,在小样本分类上具有一定优势。

A simple and efficient method based on semi-supervised learning technique is proposed for behavior modeling and abnormality detection. The method is composed of the following steps: (1) Dynamic time warping (DTW) based spectral clustering method is used to obtain a small set of samples to initialize the hidden Markov models (HMMs) of normal behaviors; (2) The HMMs’ parameters are further trained by the method of iterative learning from a large data set; (3) Maximum a posteriori (MAP) adaptation technique...

A simple and efficient method based on semi-supervised learning technique is proposed for behavior modeling and abnormality detection. The method is composed of the following steps: (1) Dynamic time warping (DTW) based spectral clustering method is used to obtain a small set of samples to initialize the hidden Markov models (HMMs) of normal behaviors; (2) The HMMs’ parameters are further trained by the method of iterative learning from a large data set; (3) Maximum a posteriori (MAP) adaptation technique is used to estimate the HMMs’ parameters of abnormal behaviors from those of normal behaviors; (4) The topological structure of HMM is finally constructed to detect abnormal behaviors. The main characteristic of the proposed method is that it can automatically select the number of normal behavior patterns and samples from the training dataset to build normal behavior models and can effectively avoid the running risk of over-fitting when the HMMs of abnormal behaviors are learned from sparse data. Experimental results demonstrate the effectiveness of the proposed method in comparison with other related works in the literature.

提出了一种基于半监督学习的行为建模与异常检测方法.该算法包括以下几个主要步骤:(1)通过基于动态时间归整(DTW)的谱聚类方法获取适量的正常行为样本,对正常行为的隐马尔可夫模型(HMM)进行初始化;(2)通过迭代学习的方法在大样本下进一步训练这些隐马尔可夫模型参数;(3)以监督的方式,利用最大后验(MAP)自适应方法估计异常行为的隐马尔可夫模型参数;(4)建立行为的隐马尔可夫拓扑结构模型,用于异常检测.该方法的主要特点是:能够自动地选择正常行为模式的种类和样本以建立正常行为模型;能够在较少样本的情况下避免隐马尔可夫模型欠学习的问题,建立有效的异常行为模型.实验结果表明,该算法与其他方法相比具有更高的可靠性.

 
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