Pattern recognition of support vector network with supervising learning is studied in this paper.
研究了有监督学习支持向量网络的的模式识别；
Much of the work on digit recognition has been done in the neural network community, but more recently support vector machines have proven to be even better classifiers.
大部分数字识别的工作可以由神经网络来完成，但最近支持向量机也被证明可以在分类方面做得更好。
The prediction method of network delays based on support vector machine (SVM) was put forward.
进而提出了基于支持向量机(SVM)的网络延时预测方法。
The features of gene expression are extracted by the wavelet multi-resolution analysis, the features are classified by the support vector machines and BP neural network methods.
采用小波多分辩率分析方法提取基因表达的特征，利用支持向量机和BP神经网络方法进行分类。
A new algorithm for modeling regression curve is put forward in the paper, it combines B-spline network with improved support vector regression.
将改进的支持向量回归机与B -样条网络相结合，提出了一种建立回归曲线模型的新算法。
A network traffic anomaly detection mechanism is presented based on support vector machine (SVM).
提出了一种基于支持向量机的网络流量异常检测方法。
At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.
同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K -NN、BP神经网络和支持向量机分类器。
The paper presents a method of pose-varied face recognition based on neural network and hierarchical support vector machines.
提出了一种基于神经网络和层次支持向量机的多姿态人脸识别方法。
That the support vector machine network is applied to recognize the nonlinear fluorescence spectrum of impurities of different concentrations in air is proposed.
提出将支持向量机网络应用于含不同浓度杂质气体的非线性荧光光谱的识别。
In this paper, a method for converting GPS height to normal one by means of support vector machine is proposed, and compared with the methods of neural network, polynomial fitting etc.
结合GPS测量和水准测量资料，利用支持向量机方法对GPS高程进行了转换，并与神经网络和多项式拟合等拟合的结果进行了比较，得出了一些有益的结论。
A support vector decision function ranking method (SVDFRM) is used to calculate the contribution of network behaviors features, and then important network behaviors features are extracted.
利用支持向量决策函数排序法(SVDFRM)，通过支持决策向量函数得到网络行为的特征贡献率并提取网络行为的重要特征。
Support vector machine (SVM) and the neural network are both currently hot subject in the area of machine learning technology.
支持向量机和神经网络都是目前关于机器学习技术的研究热点。
Currently, the hottest method is neural network method and support vector machine method which also have the highest accuracy rate.
目前研究最热的识别率最高的当属神经网络方法和支持向量机方法。
Particle swarm optimization- support vector machine classification has slightly better result than self-organizing neural networks, but the complexity of network was increased.
粒子群优化支持向量机分类效果稍好，但是增加了网络的复杂度；
This paper try to predict soil erosion with the Least Square support vector machine technology and the better predict precision compared to the BP artificial neural network has been gotten.
首次尝试将最小二乘支持向量机技术用于土壤侵蚀预测，并与BP神经网络的方法进行了对比，取得了较好的预测精度。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
The result shows that the model has higher prediction accuracy. (2)The autoregressive model, BP neural network model and support vector machine model are studied in the paper, respectively.
分别对自回归模型、神经网络模型、支持向量机模型进行研究，以我国人口增长率为例，对人口增长率进行预测。
A novel adaptive support vector regression neural network (SVR-NN) is proposed, which combines respectively merits of support vector machines and a neural network.
一种新的自适应支持向量回归神经网络(SVR - NN)提出，它结合了分别支持向量机和神经网络的优点。
The generalization error of Support Vector Machine is approximately equal to that of Probabilistic Neural Network. And Support Vector Machine is easier to use than Neural Networks.
支持向量机的分类误差与概率神经网络相近，但支持向量机的使用较概率神经网络简单。
The self-organizing neural network classifier and particle swarm optimization-support vector machine were designed by author in this paper to use as classification method of motor imagery EEG.
其中自组织神经网络分类器和粒子群优化支持向量机是本文新设计的两种运动想象EEG分类方法。
Machine learning methods, including Support Vector Machines and Artificial Neural Network, are applied to the development of the classification models for the selective COX-2 inhibitors in this paper.
本文用支持矢量学习机和神经网络两种机器学习方法建立选择性环氧化酶-2抑制剂的活性预测模型，以期为选择性环氧化酶-2抑制剂药物的合成提供先导化合物。
Machine learning methods, including Support Vector Machines and Artificial Neural Network, are applied to the development of the classification models for the selective COX-2 inhibitors in this paper.
本文用支持矢量学习机和神经网络两种机器学习方法建立选择性环氧化酶-2抑制剂的活性预测模型，以期为选择性环氧化酶-2抑制剂药物的合成提供先导化合物。
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