k nearest neighbor

In this paper we develop an efficient method in order to process k nearest neighbor queries ( K-NN) in a mobile environment.

本文提出了一个以处理动态环境下的K个最近邻居（K-NN）查询问题的有效的方法。

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With the traditional k nearest neighbor method and fuzzy k neighbor classifier comparison shows that the method can not only improve the classification performance, but also can improve the classifier speed.

与传统k近邻分类算法和模糊粗糙k近邻分类算法相比，该方法不仅可以改善分类性能，而且可以提高分类器的速度。

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Searches based on content similarities in large multimedia libraries are essentially K nearest neighbor searches in high dimensional spaces.

大容量多媒体数据库的基于内容相似性的检索本质上是高维特征空间中一定距离函数的K近邻问题。

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In this step, we use the strategy of combining the k nearest neighbor classifier and mutual information to judge if the two ROIs are visual similar.

在第二层的精筛阶段，利用最近邻分类器和互信息相结合的策略来度量两个ROI在视觉上是否相似。

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This paper presents an algorithm based on the combination of Support Vector Maching ( SVM) and k Nearest neighbor ( k NN), to deal with ambiguities in Chinese word segmentation.

本文提出了基于支持向量机（SVM）和k近邻（kNN）相结合的一种分类方法，用于解决交集型伪歧义字段。

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Euclidean distance is used as similarity measurement. Range query and k nearest neighbor query algorithms are presented.

然后用多维索引结构R树存储这些特征向量，将欧几里德距离作为相似尺度，在此基础上实现了范围查询和k近邻查询。

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

最后利用K最近邻方法对汉语情感语料进行识别。

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This paper presents a modified K nearest neighbor ( MK NN) track correlation algorithm applying to distributed multisensor data fusion.

提出一种适合于分布式多传感器数据融合的新的航迹相关算法（MK－NN）。

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

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。

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At present, there are many pattern classification algorithms, mainly including bayes classifier, support vector machine, K nearest neighbor method and so on.

目前，模式分类的算法比较多，常用的有贝叶斯分类器、支持向量机、K近邻法等。

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Scalable processing of multiple continuous k nearest neighbor queries in road networks

可伸缩的道路网络多连续k近邻查询处理

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

并提出将k最近邻分类方法用于分类结果融合；

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K nearest neighbor ( kNN) query is one of the fundamental issues in spatial database research area.

k最近邻居问题（kNN）是空间数据库研究领域最基本的问题之一，是指在特定空间中寻找离查询点最近的k个目标点，包括几何空间，道路空间等。

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We measure the importance of bins on some level by K nearest neighbor entropy, then we remove those bins whose k nearest neighbor entropy bigger than threshold.

我们通过K近邻信息熵来衡量某个层次上格子的重要程度，并移除那些K近邻信息熵大于此层次阈值的格子。

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The statistic approaches, for example, distance, Fisher, k nearest neighbor, wise linear classifiers, fail to solve multi regional distributions such as the alternate table problems.

当处理同类别多区域样本分布问题，例如变标签问题时，距离判别、Fisher判别、k-近邻分类、分段线性分类等统计分析方法遇到困难。

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K nearest neighbor query on moving objects based on road networks mainly has two challenges, manage efficiently moving objects position update and offer fast network distance calculations.

基于路网的移动对象K近邻查询主要有两个挑战，分别是有效地管理移动对象位置更新和提供快速的网络距离计算。

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· Inspired by the idea of k nearest neighbor, a multi-label classification algorithm based on k mutual neighbors is presented.

基于k近邻的思想，提出了一种基于k互邻居的多标签分类算法。

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An overview of classification technology and analysis of the main classification algorithm, focuses on the principle of k nearest neighbor classification algorithm and the development present. 2.

本文主要包含了以下几个方面的内容：1.概述了数据挖掘中的分类技术，深入分析了分类挖掘的主要算法，重点介绍了k近邻分类算法的原理及发展现状。

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It is shown that the classification performance is better than the traditional k nearest neighbor algorithm from the simulation experiment. 3.

仿真实验证明该方法的分类性能优于传统k近邻方法的分类性能。

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Considering easily implement and fast calculation, the K nearest neighbor classifier and Naive Bayes classifi-er are utilized with a high classification accuracy.

出于算法实现简单、计算速度快等方面的考虑，本文选择K最近邻分类器与朴素贝叶斯分类器应用于实验数据，并获得了较高的分类准确率。

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Therefore, moving object index strategy and K nearest neighbor query strategy need to be studied.

因此需要研究基于路网的移动对象索引策略和K近邻查询策略，如何采用快速、有效的方法来处理移动对象K近邻查询是一个具有挑战性的研究课题。

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VA-Trie: A New and Efficient High Dimensional Index Structure for Approximate k Nearest Neighbor Query

VA-Trie：一种用于近似k近邻查询的高维索引结构

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A method to predict the subcellular location of proteins is proposed based on the LZ complexity similarity of symbolic sequences and K nearest neighbor rule.

提出了一个基于符号序列LZ复杂性相似度和K近邻规则的蛋白质亚细胞位点类型预测的方法。

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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

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

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。

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This technology which combines Decision Trees and K Nearest Neighbor, can mine the relative similarity of latent-item in the course of case search.

该方法将决策树法和近邻检索法等数据挖掘技术相结合，对事例检索过程中必需的特征项相似度进行挖掘。

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In order to reduce the effect of dielectric coefficient and magnetic inductive capacity on pipeline robot's localization, an N order K nearest neighbor ( K-NN) electromagnetic wave localization algorithm is presented.

为了减小传输介质介电常数、磁导率参数对管道机器人定位的影响，提出了N次K-NN分类算法。

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K Nearest Neighbor ( KNN) Method Used in Feature Selection

用于特征筛选的最近邻（KNN）法

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To improve the design of classifier and training time, a new method combining the clonal selection algorithm and K nearest neighbor ( CSA+ KNN) is proposed.

针对植物叶片识别中分类器设计和训练识别时间较长的问题，提出了一种基于克隆选择算法和K近邻判别分析（CSA+KNN）的叶片识别方法。

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Considering K Nearest Neighbor ( KNN) method has high accuracy but poor efficiency, this paper proposes a text categorization method based on the guidance of posterior probability named B-KNN.

针对K最近邻（KNN）方法分类准确率高但分类效率较低的特点，提出基于后验概率制导的贝叶斯K最近邻（B-KNN）方法。

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