最近邻

为最近邻媒体中心的概念方法是设计一个建筑，体现了美德和青春活力，在最近邻的报告描绘的丰富经验。

The conceptual approach for the KNN Media Center is to design a building that embodies the virtue and youthful energy depicted in KNN 's vast experience of reporting .

并提出将k最近邻分类方法用于分类结果融合；

We also introduce a simple confusing data method : K nearest neighbor algorithm .

基于最近邻聚类的INTERNET信息检索系统

Information Retrieval System for Internet based on Nearest Neighbor-clustering

一种基于k最近邻的快速文本分类方法

A Fast Text Categorization Approach Based on k - Nearest Neighbor

基于决策树和K最近邻算法的文本分类研究

Study on Text Categorization Based on Decision Tree and K Nearest Neighbors

一种基于Rough集理论的最近邻协同过滤算法

A Nearest-Neighbor Collaborative Filtering Algorithm Based on Rough Set Theory

最后利用K最近邻方法对汉语情感语料进行识别。

Finally , an emotion recognition experiment based on K Nearest Neighbor is described .

道路网络中移动对象的连续反k最近邻查询算法

An algorithm for continuous reverse k-nearest neighbor queries of moving objects in road network

P2P环境中k最近邻搜索算法研究

Research on k-nearest NeighBor Search Algorithm in P2P

本文利用K最近邻方法的思想，提出了一种基于K最近邻的关键词自动抽取方法。

In this paper , we proposed an automatic keyword extraction method based on KNN method .

综合最近邻法和最小二乘法，定义了β、g和σ2的估计量，在适当的条件下证明了σ2的估计量的渐进正态性。

As a result , the asymptotic normality of σ 2 is obtained under some suitable conditions .

NA样本最近邻密度估计的相合性

Consistency of nearest neighbor estimator of density function for negative associated samples

移动点对象HR索引及反向最近邻查询

HR Indexing of Moving Point Objects and Reverse Nearest Neighbors Query

RBF神经网络采用最近邻聚类学习算法进行训练。

The RBF neural network is trained by the nearest neighbor-clustering algorithm .

首先改进了原始的K最近邻检测方法，使其更适合于对计算机病毒进行预测。

First improved the primal K-nearest neighbor algorithm , which can make it more suitable for computer virus detection .

然后分析子集的固有特性，计算每个数据对象的第k最近邻距离；

Secondly the inherent features of sub-data sets are analyzed and the k-nearest neighbor distance of every point is computed .

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。

The results are compared with the genetic algorithm in combination with the k-nearest neighbor ( KNN ) classification rule .

系统的平均谱刚度分布比最近邻能级间距分布对k的灵敏度更强。

The average spectral rigidity distribution of the system has greater impact by κ than the nearest-neighbor-spacing distribution ( NNSD ) .

介绍了流型辨识的几种主要的方法：最近邻法、K近邻算法、神经网络法和特征提取法。

Several major flow pattern identification methods are introduced : nearest neighbor method , K nearest neighbor , neural network and feature extraction method .

在计入最近邻二体和三体作用的简谐近似下，解出了N×N正方简单格子的振动模。

Taking account of two - and three-body interaction of the nearestneighbours , the acoustic modes of N × N square lattice is solved in harmonic approximation .

简要分析RBF网络的结构特点及最近邻聚类学习算法，以大量粉土地基实测数据为学习训练样本及预测样本，建立了预测模型。

The structure features of radial basis function and the nearest neighbor-clustering algorithm have been discussed in detail .

为了提高系统检索的效率和准确度，事例检索的方法按照索引、关键字、K最近邻法的顺序进行。

In order to improve the efficiency and accuracy of search , the order of case retrieval is index , keyword and K nearest neighbor .

提出了一种K-最近邻改进算法，该算法用模糊自适应共振理论（FuzzyART）对K-最近邻的训练样本集进行浓缩，以改善K-最近邻的计算速度。

This paper implemented an improved K-Nearest Neighbor ( Fuzzy KNN ) algorithm in which Fuzzy Adaptive Resonance Theory ( ART ) is applied in K-NN classification to make a new algorithm .

基于Voronoi图的反向最近邻查询

Reverse Nearest Neighbor Search Based on Voronoi Diagram

最近邻模式识别法在车载FSK信号检测中的应用

Nearest-neighbor Pattern Recognition in Demodulation of Railway FSK Signal

一种基于动态最近邻聚类算法RBF网络非线性系统复合控制器设计

The design of a multiplexed controller for use with a nonlinear system based on dynamic nearest neighbor-clustering algorithm for RBF neural networks

用获取的不同网络服务的时频特性分布作为训练样本，训练后的K最近邻分类器可实现网络故障的识别。

Training samples are acquired from the time-frequency characteristic distribution of different network services . A K-nearest neighbor classifier is used to identify the system faults after being trained .

最近邻VQ码本法方言识别研究

A Study of Dialect Recognition with the Nearest-Neighbor Classifier ( NNC ) on VQ Codebooks

基于SOFM和快速最近邻搜索的网络入侵检测系统与攻击分析

Network Intrusion Detection and Attack Analysis Based on SOFM with Fast Nearest-Neighbor Search

再用ICP算法进行精细配准，采用最近邻分类器进行分类。

And ICP algorithm is employed for fine registration . Finally , nearest neighbor classifier is adopted as the evaluation method .