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knn，即k最近邻算法是模式识别中的一种比较简单而经典的分类算法-Knn, or k-nearest neighbor algrithom, is a simple and classical classifier algrithom.
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knn，即k最近邻算法是模式识别中的一种比较简单而经典的分类算法-knn, k-nearest neighbor pattern recognition algorithm is a relatively simple and classic classification algorithm
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脱机手写体识别Matlab源程序
包括特征提取、bayes分类器、K近邻分类及最近邻分类。
Testscr iptRecognition.m：测试代码
scr iptFeaExtract.m ：特征提取
KNearestEstimate.m ：K近邻估计
NearestEstimate.m : 最近邻估计
BayesTrain.m ：训练bayes分类器
Bayes.m ：测试bayes分类器
CrossValidate.m ：m交叉验证
-Offlin
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c++实现的KNN库:建立高维度的K-d tree,实现K邻域搜索，最小半径搜索-K-NN algorithm implementation.
It supports data structures and algorithms for both exact and approximate nearest neighbor searching in arbitrarily high dimensions.
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K近邻算法（KNN）的matlab源代码，程序清晰易读-K nearest neighbor (KNN) of matlab source code, procedures legible
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本算法是实现基于KNN的基因遗传算法，是对KNN算法的改进，具有更好的分类效果。-gaKnn[Genetic Algorithm Optimized K Nearest Neighbor Classification framework] is a frameowork for KNN optimization with a genetic algorithm.
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knn (k-nearest neighbor)用c++实现的近邻算法-knn (k-nearest neighbor) algorithm
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k近邻算法(knn, k nearest neighbor)-k nearest neighbor (knn, k nearest neighbor)
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K近邻算法，对一段数据进行分类，word 说明文档-K nearest neighbor algorithm
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传统基本K-最邻近分类算法源程序 云计算-Traditional basic K-nearest neighbor classification algorithm source code
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java语言实现的KNN算法代码。
KNN就是K最近邻(k-Nearest Neighbor，KNN)分类算法-java language code of the KNN algorithm. KNN is a K-nearest neighbor (k-Nearest Neighbor, KNN) classification algorithm
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关于K近邻算法的详细描述，包括算法原理及应用背景。-K-nearest neighbor algorithm on a detailed descr iption, including algorithm theory and application background.
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K近邻法对Iris数据分类，输入分类结果和准确率。-K-nearest neighbor method for Iris data classification, enter the classification results and accuracy.
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Delphi realization of k-nearest neighbor algorithm.
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利用K近邻法实现数字识别算法。误差小，识别效率高，网络训练速度快。-K-nearest neighbor algorithm, digital identification algorithm. Error is small, high recognition efficiency and speed of network training.
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模式识别中用于完成数据的分类而用到的一种方法-k近邻。是将已有数据划分到3个类中，本方法中解决数据Iris数据的划分问题。将150个4维数据划分到3类。K近邻法是求最近的K个元素从而将其划分到已有类中。-Pattern recognition for the completion of the classification of the data used in a way-k neighbors. The existing data are divided into three classes
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k近邻法分类iris数据。iris数据共分三类，每一类50个数据，这里把每一类前20个作为训练样本，后30个作为测试样本-k-nearest-neighbor classification iris data. iris data is divided into three categories, each category of data from 50, as the training samples in each category of the top 20 after 30 as th
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采用快速K近邻与Kmeans聚类算法来计算前K个近邻，舍弃了一部分不可能成为待测样本的前K个近邻的训练样本，从而减少了计算量，提高了分类速度-Fast K-nearest neighbor Kmeans clustering algorithm to calculate the K nearest neighbors, abandoning the training samples of the part can not become the first K neighbors of the t
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用平均值取代表点的方法和K近邻法对Iris花进行分类-With the average of the representative point method and K-nearest neighbor to classify Iris flower
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通过k近邻算法实现数字识别，主要包含0-9之间的所有数字。(Digital identification is realized by K nearest neighbor algorithm.)
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