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rbf kernel
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  rbf核
     The result shows that the misclassified numbers of 3 sets compounds are 0,2,0 for C-SVC with RBF kernel and 9,17,7 for train-set while the 3 sets of parameters are c=512 γ=2,c=512 γ=0.248 and c=512 γ=0.512 respectively.
     结果表明,选用RBF核函数和C-SVC方法,3组参数分别为C=512、γ=2.048,C=512、γ=2.048,C=512、γ=0.512时,建立3个体系的SVM分类模型对全部样本的错误误别个数分别为0、2、0个,训练集模型对全部样本的错误识别个数分别为9,17,7。
短句来源
     SVM with RBF kernel and its application research
     RBF核SVM及其应用研究
短句来源
     A SVM Approach to Ship Power Load Forecasting based on RBF Kernel
     基于RBF核的船舶电力负荷预测SVM方法
     With RBF kernel function,the back estimation rates of three models are all 100%;
     在RBF核函数下,所建立的模型最佳,3个模型的回判率都达到100%;
短句来源
     Because of good properties of RBF kernel,SVM with RBF kernel(RBF-SVM) shows good learning performance in the practical application. But the performance of RBF-SVM is influenced greatly by the scale parameter.
     因其核函数的良好性态,RBF核SVM(RBF-SVM)在实际应用中表现出良好的学习性能,但是RBF核函数中的参数对SVM的性能起决定性作用。
短句来源
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  径向基核
     When the parameter C=512 in SVM and γ=0.000 5 in RBF kernel are used, the ratio of the samples correctly identified by the SVM to total samples is 94.59% and the ratio for LOO SVM is 91.89%.
     当选择SVM参数C=512及径向基核函数参数γ=0 5×10-3时,SVM对PVC耐蚀性能分类的模型识别率为94 59%,LOO识别率为91 89%.
短句来源
     The experiment shows that it achieves the highest recognition rate of 98.701% when using Fisher discriminant analysis algorithm based on RBF kernel function to recognize the ear image.
     实验表明:采用基于径向基核函数的Fisher判别分析算法对人耳图像进行识别,其识别率最高,为98.701%。
短句来源
     the RBF kernel function with the width of 1 is the most efficient. The precision、recall、F-measure are 0.85、0.8、0.83.
     支持向量机采用参数为1时的径向基核函数具有较好的分类效果,分类结果精度、召回率、F-measure分别达到0.85、0.8、0.83。
短句来源
     THE PARAMETER ESTIMATION OF RBF KERNEL FUNCTION BASED ON VARIOGRAM
     基于变异函数的径向基核函数参数估计
短句来源
     A performance comparison of SVMs based on Fourier kernel and RBF kernel
     基于傅立叶核与径向基核的支持向量机性能之比较
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  rbf核的
     Model Selection of SVM with RBF Kernel and its Application
     基于RBF核的SVM的模型选择及其应用
短句来源
     Optimization of SVM with RBF Kernel and Its Application on Protein Secondary Structure Prediction
     基于RBF核的SVM学习算法优化及其在蛋白质二级结构预测中的应用
短句来源
     This thesis analyses Grid Search Method (GSM) and Bilinear Search Method (BSM). By improving the two methods, we propose a new parameters optimization method of SVM based on RBF kernel——Bilinear Grid Search Method (BGSM). BGSM combines the advantages of GSM and BSM, appearing to be a more effective learning algorithm.
     针对此现状,本文分析了现有的网格搜索法(Grid Search Method, GSM)和双线性搜索法(Bilinear Search Method, BSM),并对GSM和BSM进行了改进,提出了一种新的基于RBF核的SVM学习算法的参数优化方法——双线性网格搜索法(Bilinear Grid Search Method, BGSM)。
短句来源
     A SVM Approach to Ship Power Load Forecasting based on RBF Kernel
     基于RBF核的船舶电力负荷预测SVM方法
     Optimization of SVM with RBF Kernel
     基于RBF核的SVM学习算法的优化计算
短句来源
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  “rbf kernel”译为未确定词的双语例句
     The optimal configuration of parameters (σ2,γ) for the LSSVM with RBF kernel is (7, 1). With the selected parameters, the computer edge detection experiments are carried out.
     确定了高斯LSsVM的参数(σ2,γ)为(7,1),用所选参数进行了图像边缘检测实验。
短句来源
     al combined the Bayesian and PAC theory and presented some PAC-Bayesian theorems that bound the generalization error of Bayesian classifiers. Based on David McAllester's theory and geometrical arguments, Ralf Herbrich et al presented a margin bound for linear classifiers,and this bound can be used to optimized the hyperparameter of RBF kernel function.
     Ralf Herbrich等人从几何学的角度出发,给出了PAC-Bayesian理论框架下SVMs推广性能的界,并指出只要优化该界就可以优化径向基函数(Radial Basis Function,RBF)的参数,从而为SVMs在RBF内核下的参数优化问题提供了理论依据。
短句来源
     A tighter PAC-Bayesian bound for linear classifiers than which presented by Ralf Herbrich et al is presented in this paper, and the new bound can also be used to optimize the parameters of RBF kernel in SVMs.
     本文对Ralf Herbrich等人提出的界做了进一步收紧,从而得到一个更好的上界。 同样,该界也可作为RBF内核参数优化的依据。
短句来源
     Firstly, a new RBF kernel function based on WHVDM is put forward and proved positive and definite in mathematic for the high dimensional and heterogeneous datasets acquired in Intrusion Detection(ID).
     在该方法中,首先针对高维异构数据,引入WHVDM距离构造了新的RBF型核函数,并在数学上证明了该核函数的正定性,从理论上保证了该核函数的可用性。
短句来源
     By improving GSM and BSM, we propose and implement an optimization algorithm of SVM based on RBF kernel. By improving learning methods and learning strategies, BGSM demonstrates to be better learning performance and learning accuracy compared to other methods.
     本文通过改进GSM和BSM方法,设计和实现了一种以RBF为核的SVM优化算法,通过对学习方法和学习策略等方面的改进,使得BGSM具有比相关方法更好的学习性能和较高的学习精确率。
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  rbf kernel
Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances.
      
The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel.
      
First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control.
      
We selected the RBF kernel function, based on the experimental results on our learning set.
      
When training an SVM with an RBF kernel, we set gamma to the standard deviation of the entire training set.
      
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The paper studies the form of the result of SVM with covariance function as the kernel function under some conditions, and draws the conclusion that Kriging is equivalent to SVM under those conditions. Based on the conclusion, we put forward the idea of using the covariance function as a substitute for the RBF kernel function of SVM. Considering that the covariance function could not exist in some conditions, we put forward the idea of using the variogram function as a substitute for the covariance...

The paper studies the form of the result of SVM with covariance function as the kernel function under some conditions, and draws the conclusion that Kriging is equivalent to SVM under those conditions. Based on the conclusion, we put forward the idea of using the covariance function as a substitute for the RBF kernel function of SVM. Considering that the covariance function could not exist in some conditions, we put forward the idea of using the variogram function as a substitute for the covariance function and prove their equivalence. It not only solves the problem of parameter estimation of SVM kernel function, but also connects the SVM with the Kriging, which gives new statistical meaning to SVM.

研究了支持向量机 (support vector machine,SVM)方法在一定假设条件下 ,核函数取为样本协方差函数时解的具体形式 ,得出了在该假设情况下 SVM方法等价于克立格方法的结论 ,提出了用协方差函数作为 SVM核函数的思想 .考虑到在某些情况下协方差函数可能不存在 ,因此考虑用变异函数来代替协方差函数估计径向基核函数的宽度参数 .这样不仅解决了SVM中径向基核函数宽度参数的确定问题 ,而且把这种情况下的 SVM拟合与概率统计学中的克立格方法联系了起来 ,赋予了 SVM方法新的统计上的意义

Statistical learning theory (SLT) is introduced to intrusion detection (ID) and an ID method based upon support vector machine (SVM) is presented in this paper. The SVM algorithm is generalized for high dimensional and heterogeneous datasets acquired in ID and a new RBF kernel function is developed based on HVDM distance metric of heterogeneous datasets. Supervised C-SVM and unsupervised One-Class SVM algorithms utilizing kernel function are applied in detecting the intrusions hidden in the network...

Statistical learning theory (SLT) is introduced to intrusion detection (ID) and an ID method based upon support vector machine (SVM) is presented in this paper. The SVM algorithm is generalized for high dimensional and heterogeneous datasets acquired in ID and a new RBF kernel function is developed based on HVDM distance metric of heterogeneous datasets. Supervised C-SVM and unsupervised One-Class SVM algorithms utilizing kernel function are applied in detecting the intrusions hidden in the network connection records. The testing results on the DARPA data show that the method is effective and efficient.

将统计学习理论引入入侵检测研究中 ,提出了一种基于支持向量机的入侵检测方法 (SVM BasedID) 针对入侵检测所获得的高维小样本异构数据集 ,将SVM算法在这种异构数据集上进行推广 ,构造了基于异构数据集上HVDM距离定义的RBF形核函数 ,并基于这种核函数将有监督的C SVM算法和无监督One ClassSVM算法用于网络连接信息数据中的攻击检测和异常发现 ,通过对DARPA数据的检测试验结果表明提出的方法是可行的、高效的

SVM(Support Vector Machine)with RBF kernel is widely used in pattern recognition.Model selection in this class of SVMs involves two parameters:the penalty parameter C and the kernel parameter.This paper uses grid search and two-line search to select the two parameters,and combines the advantage of the two methods to the application on Handwritten English Character Recognition.Experiments are performed on the NIST database to acquire the comparison of the searching efficiency and generalized recognition...

SVM(Support Vector Machine)with RBF kernel is widely used in pattern recognition.Model selection in this class of SVMs involves two parameters:the penalty parameter C and the kernel parameter.This paper uses grid search and two-line search to select the two parameters,and combines the advantage of the two methods to the application on Handwritten English Character Recognition.Experiments are performed on the NIST database to acquire the comparison of the searching efficiency and generalized recognition rate.It's also shown that SVM with the best parameters are much better on generalized regcognition rate against the ANN(Artificial Neural Network)classifier.

使用RBF核的SVM(支持向量机)被广泛应用于模式识别中。此类SVM的模型选择取决于两个参数,其一是惩罚因子C,其二是核参数σ2。该文使用了网格搜索和双线性搜索两种方法进行参数选择,并将两者的优点综合,应用于脱机手写体英文字符识别。实验在NIST数据集上进行了验证,对搜索效率和推广识别率进行了比较。实验结果还表明使用最优参数的SVM在识别率上比使用ANN(人工神经元网络)的分类器有较大提高。

 
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