搜索资源列表
knn_matlab.rar
- knn—k近邻准则matlab实现，适合初学者,knn-k neighbors to achieve the criteria matlab
2rar.rar
- 用matlab写的最近邻和K近邻法分类器，简单易懂，适合初学者,Written with matlab and K-NN nearest neighbor classifier, easy to understand for beginners
knn.kmeans.fisher
- 常用的分类方法，包括最近邻（NN），k均值（kmeans），k近邻，Fisher线性判别。-Commonly used classification methods, including nearest-neighbor (NN), k the mean (kmeans), k neighbors, Fisher linear discriminant.
demo
- 实现数据挖掘的几个算法，包括模糊聚类，K均值，以及K近邻等聚类算法-Some of the implementation of data mining algorithms, including fuzzy clustering, K-means, as well as neighbors, such as clustering algorithm K
KNN_Classifier
- 一个matlab环境下的k近邻分类器,代码中有详细的注释说明,使用方便.-K-Nearest-Neighbor-Classifier MatLab Code
knn
- knn-K近邻法实现两分类的函数代码，输入为两类的样本特征，和待测试的样本向量，输出为分类结果。-knn-K nearest neighbor method to achieve the two categories of function code, enter the characteristics of two types of samples, and samples to be tested vector, the output for the classification.
KNN
- 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
knnaba
- natlab源程序，k近邻的程序，希望对大家有用-natlab source, k neighbor procedure, in the hope that useful
KNN
- 自己的模式识别的作业，matlab实现k近邻算法。-K-Nearest Neighbour algorithm
KNearestCls
- 模式识别中的K近邻法和快速K近邻法的VC＋＋实现-Pattern Recognition and rapid K neighbors K neighbors law VC to achieve
linjin
- 用k近邻法和剪辑近邻法分类样本点，模式识别实验内容之一-K neighbors with neighbors and editing sample points classification, pattern recognition one experiment
k-means
- K近邻算法，对一段数据进行分类，word 说明文档-K nearest neighbor algorithm
K-mean
- 最近邻分类器是一个用来聚类的算法，可以用来对iris数据进行聚类-k-means is a neanest alogorim
K-nearest
- 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.
K-negibour-method
- 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.
k-iris
- 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
k-meas
- 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
K---nearest-neighbour-classifier
- 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
K-means-IRIS
- 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
K-means
- k-means简单实现，实现了k近邻的实现，以图像的形式显示出来，简单实用-k-means simple to achieve achieve a k neighbors realized and presented in the form of an image, simple and practical