K-最近邻,K-nearest neighbor英语短句,例句大全

K-最近邻,K-nearest neighbor
1)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)。

英文短句/例句

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.Two-tier K Nearest Neighbor Algorithm Based on Active Diagnostic Recommendation基于主动诊断推荐的双层K-最近邻算法
3.A Method Research on Active Defence Technology against Virus Based on K-Nearest Neighbor Algorithm of Kernel基于核的K-最近邻算法的主动式防御研究
4.Research on k-nearest NeighBor Search Algorithm in P2PP2P环境中k最近邻搜索算法研究
5.Study on KNN arithmetic based on cluster基于簇的K最近邻(KNN)分类算法研究
6.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
7.Algorithm for Reverse k-Nearest Neighbor Queries in Spatial Network Databases空间网络数据库中反k最近邻查询算法
8.Algorithm of KNNS based on angular similarity一种基于角相似性的k-最近邻搜索算法
9.P2P-based Self-adaptive Distributed k-nearest Neighbor Search Algorithm基于P2P的自适应分布式k最近邻搜索算法
10.The results are compared with the genetic algorithm in combination with the k-nearest neighbor( KNN) classification rule.最后，将比较的结果再与基因演算法结合k个最近邻法进行比较。
11.An algorithm for continuous reverse k-nearest neighbor queries of moving objects in road network道路网络中移动对象的连续反k最近邻查询算法
12.Automatic Keyword Extraction Based on KNN for Implicit Subject Extraction适用于隐含主题抽取的K最近邻关键词自动抽取
13.Algorithm and Simulation of SVM Classifier Based on KNN Judgment基于K最近邻决策的支持向量机分类算法及仿真
14.Incremental Hypertext Classification Based on Fuzzy K-Nearest Neighbor and Evidence Theory基于模糊K最近邻和证据理论的增量式超文本分类方法
15.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
16.An Improved Adaptive K Near Neighbor Clustering Algorithm一种改进的自适应K近邻聚类算法
17.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
18.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

相关短句/例句

k-nearest neighborsk-最近邻
1.Using the quality of reducing dimensions of Hilbert curve,the paper presents an approximate k-nearest neighbors query algorithm,and analyzes the quality of the approximate k-nearest neighbors.利用Hilbert曲线的降维特性,该文提出基于Hilbert曲线近似k-最近邻查询算法AKNN,分析近似k-最近邻的误差。
2.5, SLIQ, SPRINT, association rule, K-means, K-nearest neighbors, Bayesian network, artificial neural network and genetic algorithm, and their parallelism.5,SLIQ,SPRINT,关联规则,K-平均值,K-最近邻,贝叶斯网络,人工神经网络,遗传算法及并行性进行了研究探讨,为数据挖掘研究者提供借鉴。
3)K-nearest neighbor classifierK-最邻近
4)K nearest neighborK最近邻
5)K-nearest neighborK最近邻
1.This paper put forward and carried out a text classification method using feed-forward neural network and K-nearest neighbor.提出并实现了一种结合前馈型神经网络和K最近邻的文本分类算法。
2.By analyzing the frequency and dispersion of alerts, the two-dimensional characteristic vector is obtained, and the K-nearest neighbor classifier trained by samples is used to identify DoS attacks.提出了一种针对网络信息审计系统的拒绝服务攻击(DoS)的检测算法,该算法通过分析系统告警的频率与分散度提取能够标示系统状态变化的两维特征向量,然后使用经过样本训练的K最近邻分类器检测DoS攻击。
3.This paper proposes a simple application of Support Vector Machines to the problem of building credit scoring model,and it reports better results constrasted with the classical technique of K-Nearest Neighbor.支持向量机(SupportVectorMachines,SVMs)是一个很有前途的新技术,文章将支持向量机应用到信用评估中,和古典技术K最近邻法相比得到了比较好的结果。
6)KNNk-最近邻
1.[Method] The combination forecasting method of SVR and KNN was presented.[方法]提出一种基于支持向量机回归(SVR)与K-最近邻法(KNN)的组合预测方法:以均方误差(MSE)最小为择优准则,对SVR实施核函数寻优;基于最优核函数以SVR进行描述符筛选并得到保留描述符;以"多轮末尾强制淘汰法"阐述各保留描述符对预测精度影响的程度;基于保留描述符,以不同KNN预测值反映样本集异质性并构建子模型,最后基于SVR以留一法实施组合预测。