搜索资源列表
knn
- K=5的K近邻分类，训练样本集含有30个样本,矢量长度为5-K = 5 K-neighbor classification, the training sample set containing 30 samples, vector length of 5
wavelet-transform-using-knn
- 基于双低频小波变换和k近邻分类器的人脸识别算法源程序-Dual low frequency wavelet transform and k-nearest neighbor classifier based face recognition algorithm source
K_Average
- 通过k-近邻法将点分成两类，错误率较低，该方法效果挺好-Through the k- neighbor method will point is divided into two kinds, the error rate is low, the method effect is good
k-nearest-neighbor
- 对大量文件中相关内容的搜索，k近邻查询算法及一些改进-A large number of files in the search k nearest neighbor query algorithm and some improvements
srknn
- 自己用matlab编写实现的一个k近邻算法，并有训练和测试数据，能运行。附有注释，简单清晰。-a k-nearest neighbour algorithm developped using matlab.
nn
- 最近邻算法实现 k近邻 Z为训练集，每行一个样本，n*m labZ为与Z对应的类别，列向量 Z_T为测试集，每行一个样本，p*m labZ_T为输出结果,p*1-Nearest-neighbor algorithm
classifier
- 用matlab实现Part1. 实现一个k近邻分类器，Part 2.实现一个最小二乘分类器，Part 3.实现一个支持向量机分类器，Part 4.在不同数据集上使用交叉验证选择各个算法的参数-Part1. Achieve a k-nearest neighbor classifier, Part 2. Achieve a least-squares classifier, Part 3. Implement a support vector machine classifier, Part 4.
6
- 最近邻法和K近邻法。作为分类器算法，k近邻法和最近邻法应用广泛-Nearest neighbor and K-nearest neighbor method. As a classifier algorithm, widely k nearest neighbor method nearest neighbor method and application
iris-k-nn
- Iris 是一种鸢尾属植物。在数据记录中，每组数据包含Iris花的四种属性：萼片长度，萼片宽度，花瓣长度，和花瓣宽度，三种不同的花各有50组数据. 这样总共有150组数据或模式。这里用K近邻法进行分类。-Iris is a genus Iris. In the data recording, the data containing each of the four attributes Iris Flower: sepals length, sepal width, petal length,
Nearest-neighbor-classifier
- 本程序实现了最近邻分类器的K近邻算法。实现方式是使用matlab-This program implements a nearest neighbor classifier K-nearest neighbor algorithm. Implementation using matlab
Ch02
- K近邻算法matlab实现，实例：K近邻算法改进网站的配对效果。-Classifying with k-Nearest Neighbors
knnsearch
- 非常好的K近邻算法，算法进行了优化，速度也较快-very good！
alternative-2DPCAPknn
- 基于二阶双向二维主成成分分析用k近邻分类，alternative-2dpca,2d2dpca注解详细，在本站左加阔的程序上编写的-alternative-2dpca 2d2dpca+knn
CAL500_KNN
- 使用粒子群算法优化k近邻算法，达到多标签分类的代码，使用CAL500的数据集-the multi-label classification based on evolutionary computing using KNN
Z
- 基于k近邻估计法的非参数概率密度估计论文及源代码的如何实现-K-nearest neighbor nonparametric probability density estimation method based on the estimated
K近邻互信息计算程序
- matlab用于计算K近邻互信息量程序，多变量相关性(K nearest neighbor mutual information computing program)
knn
- #k-近邻算法 实现 KNN 分类算法 # 对未知类别属性的数据集中的每个点依次执行以下操作： # (1)计算已知类别数据集中的点与当前点之间的距离 # (2)按照距离递增次序排序 # (3)选取与当前点距离最小的K个点 # (4)确定前K个点所在类别的出现频率 # (5)返回前K个点出现频率最高的类别作为当前点的预测分类(#k-Nearest Neighbor , KNN)
KNN_demon
- 最近邻法语k近邻法的例子，基于matlab平台，有助于初学者学习。(The recent example of the nearest neighbour approach to French K, based on the MATLAB platform, helps beginners to learn.)
k-nn
- k-nn算法 K-NN算法 ( K Nearest Neighbor, K近邻算法 ), 是机器学习中的一个经典算法, 比较简单且容易理解. K-NN算法通过计算新数据与训练数据特征值之间的距离, 然后选取 K (K>=1) 个距离最近的邻居进行分类或者回归. 如果K = 1 , 那么新数据将被分配给其近邻的类.(k-nnK - NN algorithm (K on his Neighbor, K Nearest Neighbor algorithm), is a classical al
K-Nearest Neighbor Classifier
- 调用于sklearn平台的K-Nearest Neighbor Classifier算法，有着较好的分类能力(The k-nearest Neighbor Classifier algorithm for sklearn platform has good classification ability.)