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1)  weighted knearest neighborhood
加权K近邻
1.
In this paper, based on the characteristics of data set, we put forward weighted knearest neighborhood method.
本文根据数据的特点,改进了传统的K近邻方法,提出了加权K近邻的方法,进一步增强了利用测井数据识别岩性的能力,并在实际应用中证明了本方法的正确性和实用性。
2)  Wavelt packets decomposition
加权K近邻法
3)  K-NN with weighted voting
加权投票K-近邻法
4)  k-nearest neighbour weights
k-最邻近权
5)  weighted nearest neighbor
加权最近邻
1.
It established different NCs based on bootstrapping technique,and evaluated the classification accuracy of every NC by different sorts of weighted nearest neighbors for mixed attributes,then the NCs with low relative generalization error rates were dynamically selected and majority voting was applied to those NCs in order to conduct the final classification results of the ensemble.
基于bootstrapp ing构建不同的个体神经网络,针对混合属性,通过不同的加权最近邻设计评估单个网络的分类精度,在此基础上动态选择误差率较小的神经网络,经过投票形成集成分类结果。
6)  K-nearest neighbor
K-最近邻
1.
Development and improvement of K-Nearest Neighbor clustering technique
K-最近邻分类技术的新发展与技术改进
2.
To further understand the quantitative structure-activity relationship(QSAR)of fluorine-containing pesticide and improve the prediction precision of QSAR models,a novel nonlinear combinatorial forecast me-thod named Multi-KNN-SVR,multi-K-nearest neighbor based on support vector regression,was proposed.
为深入认识含氟农药生物活性与其结构之间的关系,建立了理想的QSAR模型,从化合物油水分配系数等7个分子结构描述符出发,基于支持向量回归(SVR)和MSE最小原则,经自动寻找最优核函数和非线性筛选描述符,构建了多个K-最近邻(KNN)预测子模型。
3.
In order to improve the predication precision of quantitative structure-activity relationship(QSAR) model,a novel combinatorial k-nearest neighbor method based on support vector machine regression(SVR-CKNN) was proposed,which could screen descriptors automatically and then builds several k-nearest neighbor models for combinatorial forecast.
该法基于支持向量机回归(SVR)自动筛选化合物结构描述符,以k-最近邻建立多个子模型实施组合预测(CKNN)。
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