1)K-nearest neighborK近邻
1.Phosphorylation Site Prediction Based on k-Nearest Neighbor Algorithm and BLOSUM62 Matrix;基于k近邻和BLOSUM62矩阵方法的磷酸化位点预测
2.Facial expression recognition based on C-means and K-nearest neighbor algorithms;基于C均值K近邻算法的面部表情识别
3.A promising K-nearest neighbor nonparametric regression forecasting model based on typical historical database was developed.基于所构建的历史数据库,通过数值试验,确定了状态向量、距离匹配原则,K近邻值等参量,构建了一种基于K近邻的非参数回归短时交通预测模型,实现了对路段行程速度的短时预测。
英文短句/例句
1.An Improved Adaptive K Near Neighbor Clustering Algorithm一种改进的自适应K近邻聚类算法
2.Prediction of Protein Functions based on K Nearest Neighbors Method基于K近邻的蛋白质功能的预测方法
3.An application of K-nearest neighbor algorithm for the teaching of web page classificationK近邻算法在教学网页分类中的应用
4.Prediction of Moving Objects K-Nearest Neighbor Based on Fuzzy-Rough Sets;基于模糊—粗糙集的移动对象K近邻预测
5.Study on K Nearest Neighbor Queries of Moving Objects Based on Road Networks;基于路网的移动对象K近邻查询方法研究
6.Research on K Nearest Neighbors Algorithm under the Indoor WLAN基于K近邻算法的WLAN室内定位技术研究
7.Feature Selection Based on Margin of K-Nearest Neighbors基于K近邻分类间隔的特征选择方法研究
8.An Ameliorated SYM Classifying Algorithm Combined with kNN一种改进的结合K近邻法的SVM分类算法
9.K-NN, K-means and the Application in Text Mining;K-近邻、K-均值及其在文本分类中的应用
10.Web Image Mining of K-NN Classification Algorithm Using Relevance Feedback;相关反馈K-邻近分类Web图像数据挖掘
11.Research on k-nearest NeighBor Search Algorithm in P2PP2P环境中k最近邻搜索算法研究
12.Study on KNN arithmetic based on cluster基于簇的K最近邻(KNN)分类算法研究
13.Network Text Classification Based on K-Nearnest neighbor Method基于K-近邻方法的网络信息文本分类
14.Approach for pre-extracting support vectors based on k-NN基于k-最近邻的支持向量预选取方法
15.Iris Recognition Method and Its Implementation Based on k-NN Classification Matching;基于k近邻分类匹配的虹膜识别方法及实现
16.Study on Feature Transformation Algorithm Based on k-Nearest-Neighbor Classification Rule;基于k近邻分类准则的特征变换算法研究
17.Applied and Study of Two Dimensionality Reduction Based on KNN;基于K-近邻法的两种降维方法应用研究
18.Forecast of Empty Container Throughput Based on K-NN Kernel Estimation;基于K-近邻核估计的港口空箱吞吐量预测研究
相关短句/例句
KNNK近邻
1.KNN(K nearest neighbors) is one of the best text categorization algorithms based on Vector Space Model.K近邻算法是基于向量空间模型的最好的文本分类算法之一。
2.This paper discusses two popular algorithms for text categorization:Vector Space Model(VSM) and k Nearest Neighbor(kNN).论文讨论了两个常用的文本分类算法:向量空间法和k近邻方法。
3.The paper analyze the KNN(k near neighbour)algorithm,presents spam filtering Model based on kNN.本文通过对K近邻算法进行研究,在其基础上提出了一种基于K近邻的邮件过滤模型。
3)K-nearest neighborK-最近邻
1.Development and improvement of K-Nearest Neighbor clustering techniqueK-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)。
4)k-near neighbor groupk-近邻群
1.A novel combinatorial forecast method based on support vector machine regression and k-near neighbor group and its application in QSAR;基于SVR和k-近邻群的组合预测在QSAR中的应用
5)k-neighbork近邻
1.K-neighbor Searching of Surface Reconstruction From Scattered Points;散乱数据点的k近邻搜索算法
2.Moreover,to accelerate the normal vector spreading,we propose the two-order nearest distance and one-order k-neighbor method.主要思想是根据法向距离阈值,把散乱点划分为平坦点和非平坦点两种类型;根据其邻近点是否有不平坦点来采用不同的法矢传播方式而无需建立散乱点法矢的Riemannian图;并提出了两次最近距离和一次k近邻遍历法加快了法矢的传播速度。
6)k nearest neighbork近邻
延伸阅读
Nearestneighbor(近邻取样)nearest neighbor (近邻取样)又被称为point sampling(点取样),是一种较简单材质影像插补的处理方式。会使用包含像素最多部分的图素来贴图。换句话说就是哪一个图素占到最多的像素,就用那个图素来贴图。这种处理方式因为速度比较快,常被用于早期3d游戏开发,不过材质的品质较差。